Prompt and instructions
Define the task, role, constraints and output format.
The model is one component of the product
Large Language Models
LLMs interpret and generate language. They should not be confused with complete search engines, live databases, retrieval systems or autonomous agents.
A model trained to work with language
Large Language ModelAn LLM is a model trained on large amounts of language data to predict and generate text and perform language-related tasks.
Parameters are not a live source of truth
Training can encode patterns, associations and factual information in model parameters. The knowledge may be incomplete, stale or difficult to access precisely. A model can also generate plausible language without sufficient evidence.
Products therefore add retrieval, tools, system instructions, safety controls, memory and user context around the model. The resulting application is larger than the LLM itself.
Five components that change the answer
Context window
Contains the conversation, retrieved passages, tool results and other information available now.
Retriever
Selects outside documents, passages or records for the question.
Tools
Search, calculate, browse, query databases or perform authorized actions.
Interface
Controls citations, links, warnings, memory, personalization and user confirmation.
You are publishing into retrieval systems, not training a personal model
A public website should not attempt to “speak to the LLM” through hidden instructions or unnatural text. It should publish accurate, accessible evidence that search and retrieval products can discover and interpret.
The model may summarize a small passage rather than the whole page. Clear local context, explicit subjects and nearby limitations reduce the risk of a detached passage becoming misleading.
Fluency is not evidence
- Check generated claims against sources rather than judging confidence or writing quality.
- Distinguish model knowledge from retrieved evidence.
- Test freshness-sensitive questions separately.
- Record the product and mode, because the same model may behave differently with different tools.
- Do not interpret one answer as a stable database record.
Primary material behind this analysis
OECD Explanation of the Updated Definition of an AI System
A practical distinction among AI systems, models, inputs, inference, outputs, autonomy and adaptiveness.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
The original RAG paper combining parametric generation with retrieved non-parametric memory.
NIST AI Risk Management Framework: Generative AI Profile
Cross-sector guidance for governing, mapping, measuring and managing generative-AI risk.
Open the original source at National Institute of Standards and Technology →
Continue through the discovery stack
Retrieval-Augmented Generation Explained
A detailed explanation of RAG, the original architecture, modern pipelines, failure modes and the boundary between publishers and AI-product operators.
OECD Definition of an AI System
An explanation of the OECD AI-system definition and the differences among models, complete systems, inputs, inference, outputs, autonomy and adaptiveness.
AI Agents, Tool Use and Machine-Operable Websites
A guide to agent loops, browser interaction, accessible controls, confirmation, prompt injection, permissions and safe task completion.
Information Retrieval Explained
A practical guide to lexical, semantic, hybrid, graph and structured retrieval, failure modes, passage design and evaluation.