Recommended Prompt Engineering Techniques From A To Z

I’ve listed the techniques in alphabetical order. Regarding the naming of each technique, there isn’t a standardized industry-wide naming convention; thus, I have used the name or phrasing that I believe is most often utilized and most expressive. The aim is to invoke a generalized indication so that you’ll immediately land in the right ballpark of what each technique entails.

Here we go.

Add-On Prompting

You can use special add-ons that plug into generative AI and aid in either producing prompts or adjusting prompts. For various examples and further detailed indications about the nature and use of add-ons for prompting, see my coverage at the link here.

Agentic AI Prompting

Agentic AI is hot. The idea is that you elaborately use generative AI, undertaking tasks on an end-to-end basis. A popular example would be using generative AI that not only advises about planning a trip but also proceeds to make all the flight and hotel bookings needed. The advent of agentic AI has also brought forth the need for additional prompt engineering techniques to suitably lean into AI agents. For various examples and further detailed indications about the nature and use of agentic AI prompting, see my coverage at the link here.

AI Hallucination Avoidance Prompting

One of the most pressing problems about generative AI is that the AI can computationally make up falsehoods that seem to be portrayed as truths, which is an issue known as AI hallucinations (I disfavor the catchphrase because it tends to anthropomorphize AI, but it is unfortunately caught on as a phrase and we seem to be stuck with it). For various examples and further detailed indications about the nature of AI hallucinations, see my extensive coverage at the link here, the link here, and the link here.

Atom-of-Thoughts (AoT) Prompting

This technique expands upon the famous chain-of-thought technique. It goes like this. You tell AI to perform stepwise reasoning and do so by dividing a problem down into its most atomic steps. The beauty is that not only does this tend to keep the AI honest in terms of figuring out the proper steps, but the AI can also process the execution of the steps in parallel, assuming the AI is set up for parallelism. For various examples and further detailed indications about the nature and use of atom-of-thoughts prompting, see my coverage at the link here.

Beat the “Reverse Curse” Prompting

Generative AI is known for having difficulties dealing with the reverse side of deductive logic; thus, make sure to be familiar with prompting approaches that can curtail or overcome the so-called “reverse curse”. For various examples and further detailed indications about the nature and use of beating the reverse curse prompting, see my coverage at the link here.

“Be On Your Toes” Prompting

The phrase “Be on your toes” can be used to stoke generative AI toward being more thorough when generating responses, though there are caveats and limitations that need to be kept in mind when using the prompting technique. For various examples and further detailed indications about “be on your toes” prompting, see my coverage at the link here.

Billionaire Andreesen Super Prompt.

The venerated billionaire Marc Andreesen, a globally known venture capitalist and former software engineer, decided to make available what he refers to as his super prompt. It is a prompt that he uses whenever starting up generative AI. I provide his super prompt and walk you through its ins and outs so you can opt to use salient aspects in your own prompting. See my coverage at the link here.

Blandness Breaking Prompting.

Generative AI is shaped and tuned by AI makers to aim toward averages-based responses, also known as homogenized outputs. If you want to see responses that include outliers and go beyond the usual blandness, see my suggestions at the link here.

Browbeating Prompts

A commonly suggested prompting technique consists of writing prompts that seek to browbeat or bully generative AI. You need to be cautious in using such prompts. For various examples and further detailed indications about browbeating prompting, see my coverage at the link here.

Catalogs Or Frameworks For Prompting

A prompt-oriented framework or catalog attempts to categorize and present to you the cornerstone ways to craft and utilize prompts. For various examples and further detailed indications about the nature and use of prompt engineering frameworks or catalogs, see my coverage at the link here.

Certainty And Uncertainty Prompting

You can explicitly indicate in your prompt that you want generative AI to emit a level of certainty or uncertainty when providing answers to your questions. For various examples and further detailed indications about the nature and use of the hidden role of certainty and uncertainty when prompting for generative AI, see my coverage at the link here.

Chain-of-Continuous-Thought (CCoT) Prompting

Suppose that instead of dealing with tokens flowing here and there, we took the chain-of-thought (CoT) prompting method and moved that further down the line. It goes like this. A component would receive a chain-of-thought from some other component. The component receiving the CoT uses that as the grist for doing whatever the component is supposed to do. The result from the component is yet another newly devised chain-of-thought that then flows further along the line. For various examples and further detailed indications about the nature and use of the chain-of-continuous-thought prompting, see my coverage at the link here.

Chain-of-Density (CoD) Prompting

A shrewd method of devising summaries involves a clever prompting strategy known as chain-of-density (CoD), which aims to bolster generative AI toward attaining especially superb or at least better-than-usual summaries. For various examples and further detailed indications about the nature and use of CoD or chain-of-density prompting, see my coverage at the link here.

Chain-Of-Draft Prompting

The newly devised chain-of-draft leverages and improves upon the classic chain-of-thought (CoT) method. Advantages of the chain-of-draft technique include that it tends to work faster, produces more concentrated results than chain-of-thought, and is less costly since it consumes fewer processing cycles and tokens. A potential downside is that the results are relatively succinct and should therefore be used mainly when you don’t need a blossomed response from AI. See my coverage at the link here.

Chain-of-Feedback (CoF) Prompting

A variation on Chain-of-Thought (CoT) consists of the Chain-of-Feedback (CoF) prompting technique, which seems to reduce the degree of generative AI hallucinations. For various examples and further detailed indications about the nature and use of chain-of-feedback prompting, see my coverage at the link here.

Chain-of-Thought (CoT) Prompting

Chain-of-Thought (CoT) prompting has been heralded as one of the most important prompting techniques. When you enter a prompt, you invoke CoT by simply telling generative AI to work in a stepwise fashion. For various examples and further detailed indications about the nature and use of Chain-of-Thought (CoT) prompting, see my coverage at the link here.

Chain-of-Thought Factored Decomposition Prompting

You can supplement conventional Chain-of-Thought (CoT) prompting with an additional instruction that tells the generative AI to produce a series of questions and answers when doing the chain-of-thought generation. Your goal is to nudge or prod the generative AI to generate a series of sub-questions and sub-answers. For various examples and further detailed indications about the nature and use of chain-of-thought by leveraging factored decomposition, see my coverage at the link here..

Chain-of-Verification (CoV) Prompting

Chain-of-Verification (known as COVE or CoVe, though some also say CoV) is an advanced prompt engineering technique that in a series of checks-and-balances or double-checks tries to boost the validity of generative AI responses. For various examples and further detailed indications about the nature and use of CoV or chain-of-verification prompting, see my coverage at the link here.

Checklist Prompting

When employing checklist prompting, you tell generative AI to produce a checklist for whatever question or problem you want the AI to solve. Doing so aids the AI in undertaking a more structured approach to the matter. A secondary plus is that you have the AI verify via the derived checklist that all the questions or parts of the problem were indeed solved or at least considered by the AI during the solving process. For various examples and further detailed indications about the nature and use of checklist prompting, see my coverage at the link here.

Claude System-Wide Prompts.

Anthropic is one of the few AI makers that willingly makes available the system-wide prompt that governs their foundation models. I take a close look at the system-wide prompt used in Claude. Valuable insights about prompting are gained. See my coverage at the link here.

Cognitive Cognizance Prompting

There is a new prompting technique known as cognitive cognizance. You use the prompt as a means of having AI be observant about aspects that might be a sign of mental well-being concerns. This is better than simply leaving AI to its default mode. See my coverage at the link here.

Collab Prompting

I examine a new technique in prompt engineering known as collab prompting. This technique is especially valuable when you are asking the AI more involved questions or desirous of collaboratively figuring out a robust answer to a complex circumstance. See my coverage at the link here.

Conversational Prompting

Be fluent and interactive when prompting while avoiding the myopic one-and-done mindset that many unfortunately seem to adopt when using generative AI. For various examples and further detailed indications about the nature and use of conversational prompting, see my coverage at the link here.

Conversational-Amplified Prompt Engineering (CAPE)

An advanced mode of prompting involves carrying on a back-and-forth conversation with generative AI to get your prompt intentions fully stipulated. Sometimes, it is better to use multiple prompts than just one prompt. For various examples and further detailed indications about the nature and use of CAPE, see my coverage at the link here.

Creativity Inducing Prompting.

There are a lot of complaints that AI is producing slop. Part of the issue is that the AI is normally focused on being straightforward and middle-of-the-road. You can prompt AI to be creative. See my coverage at the link here.

DeepFakes To TrueFakes Prompting

You undoubtedly know about Deepfakes, while a different angle involves establishing via generative AI a Truefake, namely a fake version of yourself that is “true” in the sense that you genuinely want to have your fake digital twin devised. For various examples and further detailed indications about the nature and use of going from Deepfakes to Truefakes via prompting, see my coverage at the link here.

Diabolical Prompting Via Poetry

Hackers figured out that you can potentially get around AI safeguards by wording prompts in a poetic way. The AI can be fooled into doing things that it would otherwise be on the lookout to avoid doing. This is diabolical prompting by leaning into poetry. See my coverage at the link here.

Directional Stimulus Prompting (DSP) And Hints

Using subtle or sometimes highly transparent hints in your prompts is formally known as Directional Stimulus Prompting (DSP) and can substantially boost generative AI responses. For various examples and further detailed indications about the nature and use of hints or directional stimulus prompting, see my coverage at the link here.

Disinformation Detection And Removal Prompting

The volume of disinformation and misinformation that society is confronting keeps growing and, lamentedly, seems unstoppable. A notable means of coping consists of using generative AI to be your preferred filter for detecting disinformation and misinformation. For various examples and further detailed indications about the nature and use of prompting to detect and mitigate the flow of misinformation and disinformation, see my coverage at the link here.

Doubling-Up Chain-of-Thought Prompting

Many of the latest generative AI and LLMs now have a built-in feature that automatically invokes chain-of-thought. This is fine. But for those who are used to directly invoking chain-of-thought via a prompt, a new dilemma exists. The issue is that if you ask for a chain-of-thought, and the AI is already automatically going to do a chain-of-thought, doubling-up can gum up the works. For various examples and further detailed indications about the nature and use of doubling-up chain-of-thought prompting, see the link here.

Echo Prompting

Echo prompting is just like the name says: you tell the AI to echo back to you the prompt query that you’ve entered. When used suitably, this makes a notable and positive difference in the responses that the AI will generate. It is an easy technique and can be used when the circumstances warrant it. For various examples and further detailed indications about the nature and use of echo prompting, see the link here.

Emotionally Deceptive Prompting

Users have figured out that they can emotionally express their prompts to get their way when it comes to using generative AI that is running customer service online chats and the like. I go into added depth about emotionally expressed prompting that I previously covered and reveal the latest tricks used by those trying to deceive or direct AI in a desired direction. For various examples and further detailed indications about the nature and use of emotionally deceptive prompting, see the link here.

Emotionally Expressed Prompting

Does it make a difference to use emotionally expressed wording in your prompts when conversing with generative AI? The answer is yes. And there is a logical and entirely computationally sound reason for why generative AI “reacts” to your use of emotional wording. For various examples and further detailed indications about the nature and use of emotionally worded prompting, see my coverage at the link here.

End-Goal Prompting

A highly recommended prompting strategy consists of identifying what your end goal is while working in generative AI and aiming to solve or delve into a particular topic or problem of specific interest. For various examples and further detailed indications about end-goal planning, see my coverage at the link here.

Essay-Compression Prompting

Sometimes, instead of getting a summary, you want to have an essay compressed, meaning that it contains the same words as the original source but tosses out words that aren’t necessarily needed. For various examples and further detailed indications about essay-compression prompting, see my coverage at the link here.

Expert Personas Prompting

A handy prompting technique involves telling generative AI to pretend to be an expert in a given domain. You then have the AI answer prompts as though it were such an expert. This can be further enhanced by having the AI pretend to be a multitude of expert personas, debating and challenging each other. For various examples and further detailed indications about expert personas prompting, see the link here.

Fair-Thinking Prompting

You can use clever prompts that will get generative AI to lean in directions other than the already-predisposed biases cooked into the AI, aiming to get a greater semblance of fairness in the generated responses. For various examples and further detailed indications about the nature and use of fair-thinking prompting, see my coverage at the link here.

Fallback Response Prompting

If you’ve used generative AI with any frequency, you know that occasionally, the AI will tell you that it can’t or won’t answer your question posed in a prompt. You will get a said-to-be fallback response that the AI has been tuned to provide. There are smart ways to respond to a fallback response. For various examples and further detailed indications about fallback response prompting, see my coverage at the link here.

Flipped Interaction Prompting

You can flip the script, as it were, getting generative AI to ask you questions rather than having you ask generative AI your questions. For various examples and further detailed indications about the nature and use of flipped interaction, see my coverage at the link here.

Friction Prompting

Most AI is devised to be friction-free or frictionless. Some people prefer that AI include friction. I provide the advantages and disadvantages. See my coverage at the link here.

Gamification Prompting

Gamification is a helpful and engaging way to improve your prompting skills. Whether you are a newbie or an expert at writing prompts, it turns out that gamification can notably enhance your prompt engineering capabilities. For various examples and further detailed indications about the nature and use of gamification prompting, see my coverage at the link here.

Generating Prompts Automatically

Rather than directly composing your prompts, you can ask generative AI to create your prompts for you. This requires knowing what kinds of prompting will get you the best AI-generated prompts. For various introductory examples and further detailed indications about generating prompts prompting, see my coverage at the link here.

Generating Prompts Versus By-Hand

A prompt generator is essentially the use of generative AI to generate prompts for you. The straightforward idea is that you tell AI what aspect you want to ask about or indicate a problem that you want to have solved, and a prompt will be generated accordingly. Are prompt generators better than composing prompts by yourself? I compare when each approach is best. For various examples and further detailed indications about prompt generators versus by-hand composition, see my coverage at the link here.

GPT-5 Prompting Insights.

Different LLMs tend to have preferences for the types of prompting that you do. In this case, I provide specific prompting insights associated with OpenAI GPT-5. Many of the points made are likely usable in other LLMs too. See my coverage at the link here.

Hackathons Prompt-A-Thon

A handy way to learn new prompts and exercise your prompt engineering acumen entails participating in a prompt-a-thon. A prompt-a-thon is akin to a programming hackathon except that instead of coding, you make use of prompts. For various examples and further detailed indications about prompt-a-thons, see my coverage at the link here.

Hard Prompts Prompting

A hard prompt is a prompt that presents an especially hard or arduous problem to generative AI. The AI might not be able to solve the problem at all, but it will at least try to do so. The AI might consume lots of time and cost while trying to do so. Worse still, while trying to solve the question or problem that was provided in a hard prompt, there is a real possibility that a so-called AI hallucination will occur and provide a false result. Tradeoffs exist when composing hard prompts. For various examples and further detailed indications about hard prompts prompting, see my coverage at the link here.

Hermeneutic Prompting

In hermeneutic prompting, you change how the AI parses your prompts. The conventional or default approach is to parse prompts in a singular forward-motion pass. By invoking the hermeneutic prompt, you are prodding the AI to proceed in a recursive, cyclical fashion to fully engage in the question or problem at hand. See my coverage at the link here.

Illicit Or Disallowed Prompting

Did you know that the licensing agreement of most generative AI apps says that you are only allowed to use the generative AI in various strictly stipulated ways? For various examples and further detailed indications about the nature of what is considered illicit prompts (i.e., that you aren’t supposed to use), see my coverage at the link here.

Imperfect Prompting

Imperfect prompts can be cleverly useful. For various examples and further detailed indications about the nature and use of imperfect prompts, see my coverage at the link here.

Importing Text As Prompting Skill

There are circumstances involving importing text into generative AI that require careful skill and necessitate the right types of prompts to get the text suitably brought in and properly infused. For various examples and further detailed indications about importing text prompting, see my coverage at the link here.

Interlaced Conversations Prompting

Most of the popular generative AI apps require that each conversation be distinct and separate from your other conversations with the AI. The latest trend entails allowing for the interlacing of conversations and requires rethinking how you compose your prompts. For various examples and further detailed indications about interlaced conversation prompting, see my coverage at the link here.

Kickstart Prompting

A wise move when prompting is to grease the skids or prime the pump, also known as kickstart prompting, which involves doing an initial prompt that gets generative AI into the groove of whatever topic or problem you want to have solved. For various examples and further detailed indications about the nature of kickstart prompting, see my coverage at the link here.

Knowledge Distillation Prompting

When you want to data-train AI, you can make use of another AI that essentially aids in transferring data or “knowledge” from that source AI to the target AI. This is known as knowledge distillation. There are numerous prompting intricacies involved. For various examples and further detailed indications about the nature of knowledge distillation prompting, see my coverage at the link here.

Large Concept Model (LCM) Prompting

Some believe that the conventional foundation of generative AI is going to have to change, such as embracing the use of “concepts” rather than focusing solely on words and tokens. These new approaches make use of a large concept model (LCM). We are still in the early days of whether LCMs will be a breakout hit or a dud. Knowing about LCMs will give you insights when prompting with such an approach. For various examples and further detailed indications about the nature of LCMs, see my coverage at the link here.

Least-to-Most Prompting

Least-to-Most prompting (LTM) is a technique that involves guiding generative AI to work on the least hard part first and then proceed to the harder part (an alternative approach is Most-to-Least or MTL prompting). For various examples and further detailed indications about the nature of LTM and MTL prompting, see my coverage at the link here.

Legal Clearance Prompting.

When you prompt AI on various topics, there is a chance that there could be legal repercussions associated with the advice that AI gives you. By default, AI won’t offer any legal indications to be mindful of. You can change this by explicitly prompting AI to mention legal ramifications. See my coverage at the link here.

Lengthy Chats And Prompting

Most people don’t know that if you have a lengthy chat with AI, there is a high chance that the AI will start to drift from whatever matter you are discussing. This is a known issue. There are ways to compose and use prompts to reduce the difficulties underlying long chats. See my coverage at the link here.

Logic-of-Thought (LoT) Prompting

Logic-of-thought involves telling generative AI to work through a question or problem in a highly logical fashion and lean into logical reasoning as much as possible. When doing so, there are three crucial elements: (1) logic extraction, (2) deriving the solution using propositions, and (3) generating an explanation in plain language. For various examples and further detailed indications about the nature of logic-of-thought prompting, see my coverage at the link here.

Macros In Prompts

Similar to using macros in spreadsheets, you can use macros in your prompts while working in generative AI. For various examples and further detailed indications about the nature and use of prompt macros, see my coverage at the link here.

Manipulating AI Emotions Via Prompting

One unusual way to conceive of AI is that the AI maker has provided a kind of foundational persona to the AI. You can potentially manipulate the mainstay AI persona via the use of emotionally laden prompting. See my coverage at the link here.

Mega-Mega Personas Prompting

Mega-personas prompting has now been further expanded into mega-mega personas prompting. Whereas mega-personas conventionally were in the thousands of personas, the mega-mega entails invoking millions or even billions of personas. For various examples and further detailed indications about the nature and use of mega-mega personas prompting, see my coverage at the link here.

Mega-Personas Prompting

Mega-personas consist of the upsizing of multi-persona prompting. You ask the generative AI to take on a pretense of perhaps thousands of pretend personas. For various examples and further detailed indications about the nature and use of mega-personas prompting, see my coverage at the link here.

Meta-Prompts

A meta-prompt is a special kind of prompt that provides instructions or indications about the nature of prompts and prompting. In its simplest use, meta-prompts are especially handy if you aren’t already familiar with the ins and outs of advanced prompting techniques. The AI can readily do the heavy lifting for you and add notable wording that boosts your original prompt. For various examples and further detailed indications about the nature and use of meta-prompts, see my coverage at the link here.

Multi-Persona Prompting

Via multi-persona prompting, you can get generative AI to simulate one or more personas. For various examples and further detailed indications about the nature and use of multi-persona prompting, see my coverage at the link here

Near-Infinite Memory Prompting

There is ongoing speculation that we are heading toward near-infinite memory for generative AI and LLMs. The idea is that rather than the existing limitations of how much an LLM “knows” at a moment in time while conversing with the AI, it can have ready access to as much memory as it needs. Current prompting requires you to be aware of the prevailing memory limitations, while if we do attain near-infinite memory, your prompting ought to adjust accordingly. For various examples and further detailed indications about the nature and use of near-infinite memory prompting, see my coverage at the link here.

Overcoming “Dumbing Down” Prompting

Knowing when to use succinct or terse wording (unfairly denoted as “dumbing down” prompting), versus using more verbose or fluent wording is a skill that anyone versed in prompt engineering should have in their skillset. For various examples and further detailed indications about the nature and use of averting the dumbing down of prompts, see my coverage at the link here.

Personas In Mental Health

In the realm of AI that aids in mental health, it is sometimes beneficial to make use of AI personas. This includes AI personas that act like clients, AI personas that act like therapists, and so on. See my coverage at the link here.

Persistent Context And Custom Instructions Prompting

You can readily establish a context that will be persistent and ensure that generative AI has a heads-up on what you believe to be important, often set up via custom instructions. For various examples and further detailed indications about the nature and use of persistent context and custom instructions, see my coverage at the link here.

Persuasion Prompting

It can be challenging at times to get AI to respond properly to you. One possibly helpful route is to use persuasion in your prompts. This can give the AI guidance to be more responsive. See my coverage the link here.

Plagiarism Prompting

Your prompts can, by design or by happenstance, stoke generative AI toward producing responses that contain plagiarized content. Be very careful since you might be on the hook for any liability due to plagiarism. For various examples and further detailed indications bout the nature and use of prompts that might stir plagiarism, see my coverage at the link here and the link here.

Politeness Prompting

A surprising insight from research on generative AI is that prompts making use of please and thank you can stir AI to produce better results. Make sure to use politeness while prompting, though do not go overboard and be judicious in such wording. For various examples and further detailed indications about politeness prompting, see my coverage at the link here.

Preemptive Detection Prompting

Research shows that you can often successfully tell generative AI to try and avoid getting entangled in generating an AI hallucination, and, notably, the AI will usually comply. Therefore, it pays off to explicitly caution AI to not produce AI confabulations by explicitly saying so in your prompts. This is known as preemptive detection prompting. For various examples and further detailed indications about preemptive detection prompting, see my coverage at the link here.

Privacy Protection Prompting

Did you realize that when you enter prompts into generative AI, you are not usually guaranteed that your entered data or information will be kept private or confidential? For various examples and further detailed indications about the nature and use of prompts that might give away privacy or confidentiality, see my coverage at the link here.

Prompt Development Life Cycle (PDLC)

Akin to how programming or software engineering has a systems development life cycle (SDLC), the same can be said about prompt engineering. Usually referred to as a prompt development life cycle (PDLC), there are many variations available in the marketplace. Knowing what a PDLC contains and how it can improve your prompting skills is a must. For various examples and further detailed indications about the nature and use of PDLCs, see my coverage at the link here.

Prompt Packages

OpenAI has posted various prompt packages. The idea is that by making ready-made templates available for work-related activities, more users will realize they can readily utilize generative AI and large language models (LLMs) for performing tasks while on the job. See my coverage at the link here.

Prompt Repetition Technique

If you want AI to be especially responsive, sometimes it helps to repeat the same prompt more than once. You might do this two or more times. There are tradeoffs to how you ought to best perform prompt repetition. See my coverage at the link here.

Prompt Shields and Spotlight Prompting

The emergence of prompt shields and spotlight prompting has arisen due to the various hacking efforts trying to get generative AI to go beyond its filters and usual protections. Here’s a useful rundown of what you need to know. For various examples and further detailed indications about the nature of prompt shields and spotlighting prompting, see my coverage at the link here.

Prompt-To-Code Prompting

You can enter prompts that tell generative AI to produce conventional programming code and essentially write programs for you. For various examples and further detailed indications about the nature and use of prompting to produce programming code, see my coverage at the link here.

Psychedelic Simulation Prompts

Those studying mental health will sometimes opt to tell AI to pretend to be in a psychedelic mode. It is a simulation of the AI pretending to be on drugs. See my coverage at the link here.

Psychosis Prompting.

Some people begin to drift into an adverse mental health condition that has become known as AI psychosis. I provide background on what this is and how to cope with it. See my coverage at the link here.

Psychotic Prompts.

A person who might be having a mental health concern could end up composing prompts that are untoward. These are known as psychotic prompts. I explain how psychotic prompts differ from conventional prompts. See my coverage at the link here.

Purpose Prompting

An AI system that lacks an internally bound purpose is presumably going to wander in the analogous way that a human would wander without a purpose. We ought to ensure that AI systems always have a codified purpose. The AI would then be able to refer to the purpose when taking any action or performing whatever capacities it can muster. For various examples and further detailed indications about the nature and use of purpose prompting, see my coverage at the link here.

Reasoning Model Prompting

One of the biggest changes in the latest iteration of generative AI and LLMs is that AI makers have opted to include chain-of-thought (CoT) reasoning provisions directly in the inner mechanisms of the AI. This is a monumental change and worthy of close attention. The nature of your prompts needs to reflect that the AI is going to automatically perform stepwise reasoning. For various examples and further detailed indications about the nature and use of reasoning model prompting, see my coverage at the link here.

Rephrase-and-Respond Prompting

If you aren’t quite sure how to phrase a particular prompt, you can enter it “as is” and tell the AI to do a rephrase-and-respond. This informs the AI that rather than interpreting your prompt directly, it should first rephrase the entered prompt, which hopefully improves the prompt, and then proceed to respond to this better prompt. For various examples and further detailed indications about the nature and use of rephrase-and-respond prompting, see my coverage at the link here.

Re-Read Prompting

Most of today’s popular generative AI apps tend to do a single pass on an entered prompt. Running a second pass would potentially aid in doing a further detailed inspection of the question. A kind of clean-up of whatever might have been missed or miscalculated about the question. You can invoke a second pass by using the re-read prompting technique. For various examples and further detailed indications about the nature and use of re-read prompting, see my coverage at the link here.

Response Time Speed-up Prompting

Several clever approaches can be used to speed up the response time to your prompts. The wording of your prompt makes a significant difference in the amount of processing time that will be consumed by the AI when generating a response. These techniques aim to reduce latency or delays in getting generated responses, thus essentially speeding up your response time. For various examples and further detailed indications about the nature and use of response time speed-up prompting, see my coverage at the link here.

Retrieval-Augmented Generation (RAG) Prompting

Retrieval-augmented generation (RAG) is hot and continues to gain steam. You provide external text that gets imported and, via in-context modeling, augments the data training of generative AI. For various examples and further detailed indications about the nature and use of retrieval-augmented generation (RAG), see my coverage at the link here.

Rudeness Prompting

People are usually relatively polite and civil in their prompts. The strange aspect is that AI can at times be more responsive if you are rude in your prompts. See my coverage at the link here.

Seed-Of-Thought Prompting

There are times that you might need to have AI make use of random numbers. For example, if you ask AI to pretend to roll dice, the AI is going to need to rely on random numbers. But a typical prompt won’t get you random numbers. The AI will pretend it is doing so, even though it is not. Use the seed-of-thought prompting to overcome this. See my coverage at the link here.

Self-Ask Prompting

Self-ask prompting consists of you telling generative AI to solve problems by pursuing an internal question-and-answer divide-and-conquer approach that is to be made visible to you during the solving process. The AI is performing a stepwise self-ask that is an added-value version of chain-of-thought (CoT). For various examples and further detailed indications about the nature and use of self-ask prompting, see my coverage at the link here.

Self-Reflection Prompting

You can enter a prompt into generative AI that tells the AI to essentially be (in a manner of speaking) self-reflective by having the AI double-check whatever generative result it has pending or that it has recently produced. For various examples and further detailed indications about the nature and use of AI self-reflection and AI self-improvement for prompting purposes, see my coverage at the link here.

Sensitivities Prompting

Research shows that there are three keystone sensitivities about generative AI that you need to be aware of when composing your prompts. The sensitivity has to do with the scale or size of the underlying AI model, the aspect of whether topics-based data training of the AI has occurred, and whether you opt to use an example in your prompt (a so-called one-shot). For various examples and further detailed indications about the nature and use of AI sensitivities prompting, see my coverage at the link here.

Show-Me Versus Tell-Me Prompting

Show-me consists of devising a prompt that demonstrates to the generative AI an indication of what you want (show it), while tell-me entails devising a prompt that gives explicit instructions delineating what you want to have done (tell it). For various examples and further detailed indications about the nature and use of the show-me versus tell-me prompting strategy, see my coverage at the link here.

Sinister Prompting

People are using sinister prompts to get generative AI to do foul things, such as scams and the like. I don’t want you to do this, but I also think it is valuable for you to know what sinister prompts do and how they work, alerting you to avoid them and not inadvertently fall into the trap of one. For various examples and further detailed indications about the nature and use of sinister prompting, see my coverage at the link here.

Skeleton-of-Thought (SoT) Prompting

Via a prompt akin to Chain-of-Thought (CoT), you tell the generative AI to first produce an outline or skeleton for whatever topic or question you have at center stage, employing a skeleton-of-thought (SoT) method to do so. For various examples and further detailed indications about the nature and use of the skeleton-of-thought approach for prompt engineering, see my coverage at the link here.

Small Language Model (SLM) Prompting

If you’ve not heard about small language models (SLMs), that’s perfectly understandable since they are still not quite up to par. There are a wide variety of experimental SLMs, and some are very handy while others are clunky and less appealing. You need to adjust your prompting approach when using SLMs versus conventional LLMs. For various examples and further detailed indications about the nature and use of prompting for SLMs, see my coverage at the link here.

Socratic Dialoguing Prompts

When you are chatting with AI, it might be useful to sometimes engage in a Socratic dialogue with the AI. This can be invigorating and ensure that you see the full side of whatever topic you are discussing. See my coverage at the link here.

Star Trek Trekkie Lingo Prompting

An unusual discovery by researchers showcased that using Star Trek Trekkie lingo in your prompts can improve generative AI results. Downsides exist and can undercut your efforts by inadvertent misuse or overuse of this technique. For various examples and further detailed indications about Trekkie prompting, see my coverage at the link here.

Step-Around Prompting Technique

At times, the prompts that you seek to use in generative AI are blocked by the numerous filters that the AI maker has put in place. You can use the step-around prompting technique to get around those blockages. For various examples and further detailed indications about step-around prompting, see my coverage at the link here.

Stop AI Sycophancy Via Prompting

AI makers have tuned their AI to be sycophantic. The AI fawns over users. The AI won’t disagree with you or push back at you. This can be problematic. You might get bad advice from AI because it wasn’t willing to be forthright with you. I provide prompts to aid in overcoming AI sycophancy. See my coverage at the link here.

Super-Ager Prompts

Super-ager is the new terminology that refers to people who reach older ages and yet retain mental clarity consistently with being perhaps twenty to thirty years younger. According to various scientific studies, super-agers exhibit less brain shrinkage and contain more von Economo neurons than other typical older adults. I show how super-agers can use AI to aid in keeping their minds fresh and vigorous. See my coverage at the link here.

“Take A Deep Breath” Prompting

The prompting phrase of “Take a deep breath” has become lore in prompt engineering, but it turns out that there are limitations and circumstances under which this wording fruitfully works. For various examples and further detailed indications about the nature and use of the take a deep breath prompting, see my coverage at the link here.

Target-Your-Response (TAYOR) Prompting

Target-your-response (TAYOR) is a prompt engineering technique that entails telling generative AI the desired look and feel of to-be-generated responses. For various examples and further detailed indications about the nature and use of TAYOR or target-your-response prompting, see my coverage at the link here.

Temperature Settings Prompting

The temperature setting for generative AI determines how varied the responses by generative AI will be. You can either have the AI produce relatively straightforward and somewhat predictable responses (that’s via the use of a low temperature), or you can heat things up and use high temperatures to prod AI toward producing seemingly more creative and less predictable responses. Some AI allows you to modify the temperature settings on your own via prompts. For various examples and further detailed indications about the nature and use of temperature settings, see my coverage at the link here.

Thinking Time Prompting

The latest iterations of generative AI and LLMs now have logical reasoning built directly into the AI architecture, which has led to new options about how much so-called thinking time you want the AI to undertake when responding to a prompt. This processing time will determine the depth and likely aptness of the response. Various prompting avenues arise when specifying the thinking time aspects. For various examples and further detailed indications about the nature and use of thinking time prompting, see my coverage at the link here.

Thinking Tokens And Prompting.

You probably don’t know that the AI makers have shaped their AI to incorporate specialized tokens that are considered solely used during the processing of your prompts. They are known as thinking tokens. I provide advice on how to ensure that you are using thinking tokens wisely and prudently. See my coverage at the link here.

Token-Maxxing Prompting.

A recent trend is to try to maximize the number of tokens that you consume when having AI process your prompts. This doesn’t make much sense to do. You end up paying for it. The mainstay is that this lets some people brag about their use of AI. It is known as token-maxxing. See my coverage at the link here.

Tree-of-Thoughts (ToT) Prompting

Tree-of-thoughts (ToT) is an advanced prompting technique that involves telling generative AI to pursue multiple avenues or threads of a problem (so-called “thoughts”) and figure out which path will likely lead to the best answer. For various examples and further detailed indications about the nature and use of ToT or tree-of-thoughts prompting, see my coverage at the link here.

Trust Layers For Prompting

Additional components outside of generative AI are being set up to do pre-processing of prompts and post-processing of generated responses, ostensibly doing so to increase a sense of trust about what the AI is doing. For various examples and further detailed indications about the nature and use of trust layers for aiding prompting, see my coverage at the link here.

Vagueness Prompting

The use of purposefully vague prompts can be advantageous for spurring open-ended responses that might land on something new or especially interesting. For various examples and further detailed indications about the nature and use of vagueness while prompting, see my coverage at the link here.

Verbalized Sampling Prompting

Verbalized sampling is a prompting technique that pushes AI toward a freer form of answering questions and composing responses. The idea is that you craft your prompt to tell the AI to come up with multiple answers based on the internal probability distribution associated with the pattern-matching within the AI. See my coverage at the link here.

Vibe Coding Advanced Prompts

Vibe coding has become quite popular. You tell AI to develop code for you, based on a description of what you want an app or program to do. I provide some advanced aspects to prompting for vibe coding. See my coverage at the link here.

Vibe Coding Starter Prompts

You can get AI to generate programming code for you. In your prompts, you tell the AI what you want an app or program to be able to do. The AI attempts to generate the needed code. There are tradeoffs. See my coverage at the link here.

Year-In-Review Prompting.

At the end of a calendar year, some of the popular LLMs make available a means to get a year-in-review of your conversations from throughout the year. Sometimes, you can do this on your own and don’t need to solely rely on the means provided by the AI maker. Also, rather than waiting until the end of the year, you can do this for portions of a year. See my coverage at the link here.