Why most AI prompts fail and what to do instead

Most people approach AI the wrong way. They spend hours tweaking prompts, adding more examples, adjusting the tone, hoping for that perfect instruction that magically gets better results. But here's what the experts know: the main thing that determines whether an Agent succeeds or fails is the quality of the context you give it. Most agent failures are not model failures anymore, they are context failures.

Instead of perfecting your prompts, you need to understand three fundamental concepts that actually control AI accuracy. Let's explore the techniques that separate amateur AI users from professionals.

What is grounding and why does it matter?

Grounding is a technique that you can use to help produce model responses that are more trustworthy, helpful, and factual. When you ground generative AI model responses, you connect them to verifiable sources of information.

Think of it this way: asking AI without grounding is like asking a friend to answer from memory. They might be right, they might be wrong, but you have no way to verify. When you ground your AI responses, you hand it the actual document and say "answer from this." Now the response is anchored to something real and verifiable.

RAG overcomes this by providing up-to-date information to LLMs. Providing "facts" to the LLM as part of the input prompt can mitigate "gen AI hallucinations."

The key insight here is simple: whenever accuracy matters, make sure the AI has something to reference. This becomes critical in business contexts where wrong information has real consequences.

What is RAG and how does it work?

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.

Here's the best analogy: imagine a student writing an essay. Without RAG, the student writes everything from memory. With RAG, the student goes to the library first, 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."

The practical workflow looks like this:

Convert documents to vectors: Data to be referenced is converted into LLM embeddings, numerical representations in the form of a large vector space. These embeddings are then stored in a vector database to allow for document retrieval.
Query processing: Given a user query, a document retriever is first called to select the most relevant documents that will be used to augment the query.
Generate grounded response: In the generative phase, the LLM draws from the augmented prompt and its internal representation of its training data to synthesize an answer.

The rule of thumb is simple: if an AI tool cites its sources, it most likely uses RAG. If it doesn't, it's writing from memory. RAG doesn't guarantee that a model won't hallucinate. It greatly reduces the risk, but doesn't necessarily eliminate it altogether.

How do you implement RAG with ChatGPT?

You can start implementing RAG with ChatGPT today using their knowledge upload feature. OpenAI's GPT documentation explains how to add files as knowledge sources.

Here's the step-by-step process:

Create a custom GPT: Open Explore GPTs in the ChatGPT sidebar or visit https://chatgpt.com/gpts. Select Create to open the GPT builder.
Add knowledge files: Knowledge lets your GPT use information from files you upload. It works best for reference material you want the GPT to draw from when answering questions, such as documentation, guides, handbooks, or internal content. Unlike instructions, which define how your GPT should behave, files uploaded as knowledge give it source material to use during a conversation.
Configure file limits: You can attach up to 20 files to a GPT. Each file can be up to 512 MB.

For more advanced implementations, you can build custom RAG pipelines using tools like LangChain with OpenAI's API. This approach utilizes ChatGPT API, LangChain, and FAISS for building a simple RAG pipeline in Python.

What is context engineering and why is it better than prompt engineering?

Prompt engineering is now being rebranded as context engineering. Yes, another fancy term to describe the important process of tuning the instructions and relevant context that an LLM needs to perform its tasks effectively.

The difference is fundamental:

Prompt engineering focuses on crafting the perfect instruction - the exact wording, tone, and structure of what you ask the AI.

Context engineering focuses on everything the AI sees before it generates a response. It's about what the model sees (docs, past chats, examples, summaries), how it sees it (structured or messy), and when it sees it (dynamically injected, static, memory-based). Context Engineering doesn't stop at prompt design — it frames the whole conversation.

Context Engineering is about providing the right information and tools, in the right format, at the right time. The core job is to ensure the model isn't missing crucial details ("Garbage In, Garbage Out").

What does effective context engineering look like?

What you are trying to achieve in context engineering is optimizing the information you are providing in the context window of the LLM. This also means filtering out noisy information, which is a science on its own, as it requires systematically measuring the performance of the LLM.

The context window includes multiple components:

System instructions: An initial set of instructions that define the behavior of the model during a conversation, can/should include examples, rules
Retrieved information: External, up-to-date knowledge, relevant information from documents, databases, or APIs to answer specific questions
Available tools: Definitions of all the functions or built-in tools it can call (e.g., check_inventory, send_email)
Conversation history: The current conversation, including user and model responses that have led to this moment

Instead of spending time perfecting your question, spend it assembling the right context.

How do you build better AI systems with these techniques?

The shift from prompt engineering to context engineering represents a maturity in how we think about AI systems. Prompt engineering makes the model easier to talk to. Context engineering makes the model safer and more useful for our business. Prompt work mostly affects the demo. Context work affects whether we can trust the system in production.

Here's your practical roadmap:

Start with grounding: For any task where accuracy matters, provide source documents rather than relying on the model's training data.
Implement basic RAG: Use Google Cloud's Vertex AI Search or Azure AI Search to add retrieval capabilities to your existing workflows.
Design context systems: Instead of writing perfect prompts for individual requests, you create systems that gather relevant details from multiple sources and organize them within the model's context window.
Measure and iterate: Context engineering involves an iterative process to optimize instructions and the context you provide an LLM to achieve a desired result. This includes having formal processes (e.g., eval pipelines) to measure whether your tactics are working.

Which businesses benefit most from these approaches?

RAG can be an extremely valuable tool for applications where highly specialized data is needed, such as for instance customer support, legal advice, or technical documentation. One typical example of a RAG application is customer support chatbots, answering customer issues based on a company's database of support documents and FAQs. Another example would be complex software or technical products with extensive troubleshooting guides. One more example would be legal advice — a RAG model would access and retrieve custom data from law libraries, previous cases, or firm guidelines.

The pattern is clear: any business that needs AI to work with specific, changing, or proprietary information benefits dramatically from these techniques.

The era of prompt engineering as we knew it is ending. Context Engineering is new term gaining traction in the AI world. The conversation is shifting from "prompt engineering" to a broader, more powerful concept: Context Engineering. Tobi Lutke describes it as "the art of providing all the context for the task to be plausibly solvable by the LLM." The question isn't whether you should learn grounding, RAG, and context engineering - it's how quickly you can start applying them to make your AI systems actually reliable.