Marketing Scoops: How To Fix Your AI Prompt Writing

What if the secret to unlocking AI’s full potential wasn’t in the technology itself but in how we use it? After spending over 200 hours teaching AI to write, Nate B Jones discovered that the biggest mistakes aren’t about algorithms or software limitations, they’re about human misunderstanding. Too often, we assume AI can read between the lines of vague instructions or magically produce brilliance without guidance. The result? Generic, uninspired content that misses the mark. But here’s the kicker: these missteps aren’t just common, they’re avoidable……..Continue reading….

By Julian Horsey

Source: Geeky Gadgets

Critics:

According to Google Research, chain-of-thought (CoT) prompting is a technique that allows large language models (LLMs) to solve a problem as a series of intermediate steps before giving a final answer. In 2022, Google Brain reported that chain-of-thought prompting improves reasoning ability by inducing the model to answer a multi-step problem with steps of reasoning that mimic a train of thought.

Chain-of-thought techniques were developed to help LLMs handle multi-step reasoning tasks, such as arithmetic or commonsense reasoning questions. For example, given the question, “Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?”, Google claims that a CoT prompt might induce the LLM to answer “A: The cafeteria had 23 apples originally.

They used 20 to make lunch. So they had 23 – 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The answer is 9.” When applied to PaLM, a 540 billion parameter language model, according to Google, CoT prompting significantly aided the model, allowing it to perform comparably with task-specific fine-tuned models on several tasks, achieving state-of-the-art results at the time on the GSM8K mathematical reasoning benchmark.

It is possible to fine-tune models on CoT reasoning datasets to enhance this capability further and stimulate better interpretability. As originally proposed by Google, each CoT prompt is accompanied by a set of input/output examples—called exemplars—to demonstrate the desired model output, making it a few-shot prompting technique. However, according to a later paper from researchers at Google and the University of Tokyo, simply appending the words “Let’s think step-by-step” was also effective, which allowed for CoT to be employed as a zero-shot technique.

In-context learning, refers to a model’s ability to temporarily learn from prompts. For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete “maison → house, chat → cat, chien →” (the expected response being dog),an approach called few-shot learning. In-context learning is an emergent ability of large language models. It is an emergent property of model scale, meaning that breaks in downstream scaling laws occur, leading to its efficacy increasing at a different rate in larger models than in smaller models. Unlike training and

fine-tuning, which produce lasting changes, in-context learning is temporary.Training models to perform in-context learning can be viewed as a form of meta-learning, or “learning to learn. Research consistently demonstrates that LLMs are highly sensitive to subtle variations in prompt formatting, structure, and linguistic properties. Some studies have shown up to 76 accuracy points across formatting changes in few-shot settings.

Linguistic features significantly influence prompt effectiveness—such as morphology, syntax, and lexico-semantic changes—which meaningfully enhance task performance across a variety of tasks. Clausal syntax, for example, improves consistency and reduces uncertainty in knowledge retrieval. This sensitivity persists even with larger model sizes, additional few-shot examples, or instruction tuning.

To address sensitivity of models and make them more robust, several methods have been proposed. FormatSpread facilitates systematic analysis by evaluating a range of plausible prompt formats, offering a more comprehensive performance interval. Similarly, PromptEval estimates performance distributions across diverse prompts, enabling robust metrics such as performance quantiles and accurate evaluations under constrained budgets.

Retrieval-augmented generation (RAG) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. It modifies interactions with an LLM so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information.

RAG improves large language models by incorporating information retrieval before generating responses. Unlike traditional LLMs that rely on static training data, RAG pulls relevant text from databases, uploaded documents, or web sources. According to Ars Technica, “RAG is a way of improving LLM performance, in essence by blending the LLM process with a web search or other document look-up process to help LLMs stick to the facts.”

This method helps reduce AI hallucinations, which have led to real-world issues like chatbots inventing policies or lawyers citing nonexistent legal cases. By dynamically retrieving information, RAG enables AI to provide more accurate responses without frequent retraining.LLMs themselves can be used to compose prompts for LLMs. The automatic prompt engineer algorithm uses one LLM to beam search over prompts for another LLM:

There are two LLMs. One is the target LLM, and another is the prompting LLM.
Prompting LLM is presented with example input-output pairs, and asked to generate instructions that could have caused a model following the instructions to generate the outputs, given the inputs.
Each of the generated instructions is used to prompt the target LLM, followed by each of the inputs. The log-probabilities of the outputs are computed and added. This is the score of the instruction.
The highest-scored instructions are given to the prompting LLM for further variations.
Repeat until some stopping criteria is reached, then output the highest-scored instructions.

CoT examples can be generated by LLM themselves. In “auto-CoT”, a library of questions are converted to vectors by a model such as BERT. The question vectors are clustered. Questions close to the centroid of each cluster are selected, in order to have a subset of diverse questions. An LLM does zero-shot CoT on each selected question. The question and the corresponding CoT answer are added to a dataset of demonstrations. These diverse demonstrations can then added to prompts for few-shot learning.

Early text-to-image models typically do not understand negation, grammar and sentence structure in the same way as large language models, and may thus require a different set of prompting techniques. The prompt “a party with no cake” may produce an image including a cake. As an alternative, negative prompts allow a user to indicate, in a separate prompt, which terms should not appear in the resulting image.

Techniques such as framing the normal prompt into a sequence-to-sequence language modeling problem can be used to automatically generate an output for the negative prompt. A text-to-image prompt commonly includes a description of the subject of the art, the desired medium (such as digital painting or photography), style (such as hyperrealistic or pop-art), lighting (such as rim lighting or crepuscular rays), color, and texture.

Word order also affects the output of a text-to-image prompt. Words closer to the start of a prompt may be emphasized more heavily. The Midjourney documentation encourages short, descriptive prompts: instead of “Show me a picture of lots of blooming California poppies, make them bright, vibrant orange, and draw them in an illustrated style with colored pencils”, an effective prompt might be “Bright orange California poppies drawn with colored pencils”

While the process of writing and refining a prompt for an LLM or generative AI shares some parallels with an iterative engineering design process, such as through discovering ‘best principles’ to reuse and discovery through reproducible experimentation, the actual learned principles and skills depend heavily on the specific model being learned rather than being generalizable across the entire field of prompt-based generative models. Such patterns are also volatile and exhibit significantly different results from seemingly insignificant prompt changes.

According to The Wall Street Journal in 2025, the job of prompt engineer was one of the hottest in 2023, but has become obsolete due to models that better intuit user intent and to company trainings. Prompt injection is a cybersecurity exploit in which adversaries craft inputs that appear legitimate but are designed to cause unintended behavior in machine learning models, particularly large language models.

This attack takes advantage of the model’s inability to distinguish between developer-defined prompts and user inputs, allowing adversaries to bypass safeguards and influence model behaviour. While LLMs are designed to follow trusted instructions, they can be manipulated into carrying out unintended responses through carefully crafted inputs.

AI Prompt Engineering is Dead: Long live AI prompt engineering”.

Language Models are Unsupervised Multitask Learners”

Paraphrase Types Elicit Prompt Engineering Capabilities”.

This horse-riding astronaut is a milestone on AI’s long road towards understanding”.

Meta open sources an AI-powered music generator”.

Mastering AI Art: A Concise Guide to Midjourney and Prompt Engineering”.

AI literacy and its implications for prompt engineering strategies”.

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts.

How Chain-of-Thought Reasoning Helps Neural Networks Compute”.

How to Turn Your Chatbot Into a Life Coach”.

Get the Best From ChatGPT With These Golden Prompts”.“

Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting”.