Designing Queries for Retrieval-Augmented Generation

Large language models are impressive, but they have a fundamental limitation: they only know what they learned during training. Ask about your company's internal procedures, last week's product updates, or customer-specific information, and the model can only guess or hallucinate. Retrieval-Augmented Generation (RAG) solves this by combining AI with real-time information retrieval, but its success hinges entirely on how you design your prompts.

Understanding the RAG Pipeline

Before diving into prompt design, understand what's happening behind the scenes. When a user asks a question in a RAG system, several steps occur:

First, the query gets transformed into a vector embedding—a mathematical representation of its semantic meaning. Second, this vector searches your knowledge base (documents, databases, wikis) for similar content. Third, the most relevant chunks get retrieved and injected into your prompt as context. Finally, the AI generates a response based on both the query and the retrieved information.

Each step presents opportunities for prompt engineering to dramatically improve results.

Query Reformulation: Teaching AI to Search Better

Users rarely phrase questions optimally for vector search. Someone might ask "Why isn't my dashboard loading?" when the documentation refers to "dashboard rendering issues" or "UI display problems." The semantic gap between user language and documentation language causes poor retrieval.

Smart RAG prompts include query reformulation as a first step:

User question: {user_query}

Before searching, reformulate this question to:

1. Use technical terminology that might appear in documentation

2. Include relevant synonyms and related concepts

3. Expand acronyms and implicit context Generate 2-3 search queries that would find the most relevant information.

This transforms vague user questions into precise search queries. "Dashboard won't load" becomes multiple targeted searches: "dashboard rendering failure," "UI loading errors," "frontend display issues." Your retrieval quality improves dramatically.

Context Framing: Setting Up Retrieved Information

How you present retrieved context to the model profoundly impacts response quality. Simply dumping documents into the prompt creates confusion—the model doesn't know what's relevant or how to prioritize information.

Effective context framing provides structure:

You are answering questions about our software using official documentation.

RETRIEVED DOCUMENTATION (in order of relevance):

[Source 1 - Installation Guide, Section 3.2] {document_chunk_1}

[Source 2 - Troubleshooting FAQ] {document_chunk_2}

[Source 3 - API Reference] {document_chunk_3}

USER QUESTION: {user_query}

Instructions:

- Base your answer primarily on the retrieved documentation above

- If multiple sources contain relevant information, synthesize them

- Cite which source(s) you're referencing (e.g., "According to the Installation Guide...")

- If the documentation doesn't fully answer the question, acknowledge what's missing

This structure tells the model exactly what information is available, where it came from, and how to use it. The ordering by relevance helps the model prioritize when information conflicts or overlaps.

Grounding Instructions: Preventing Hallucination

The biggest RAG failure mode is the AI ignoring retrieved context and generating plausible-sounding fabrications from its training data. Strong grounding instructions are essential:

CRITICAL: You must base your response ONLY on the provided documentation above. Do NOT use general knowledge or make assumptions beyond what's explicitly stated.

If the documentation doesn't contain enough information to answer fully:

- State what you CAN answer based on the docs

- Clearly identify what information is missing

- Suggest what additional documentation would be needed

NEVER fabricate details, make up procedures, or invent information not present in the sources.

These explicit constraints create accountability. The model knows it must justify responses with retrieved content, not improvise from training data.

Handling Insufficient or Contradictory Context

Real-world retrieval is messy. Sometimes your search returns irrelevant results. Sometimes multiple sources contradict each other. Your prompt must handle these scenarios gracefully.

Evaluate the retrieved documentation:

IF the documentation contains a clear answer:

- Provide a comprehensive response based on those sources

- Cite specific sources used

IF the documentation is partially relevant but incomplete:

- Answer what you can from available sources

- Explicitly state: "The available documentation covers X but doesn't address Y"

- Suggest what the user might need to search for instead

IF sources contradict each other:

- Acknowledge the contradiction

- Present both perspectives with source citations

- If one source appears more authoritative or recent, indicate this

IF the documentation is completely irrelevant:

- State clearly: "I couldn't find relevant information in our documentation for this question"

- DO NOT attempt to answer from general knowledge

This structured decision tree prevents the model from forcing answers when retrieval fails.

Citation and Traceability

Production RAG systems need traceability—users should be able to verify claims against source documents. Build citation requirements into your prompts:

When referencing information from the documentation:

- Include inline citations: (Source: Installation Guide, Section 3.2)

- If quoting directly, use quotation marks

- If paraphrasing, still cite the source

- For critical or surprising information, quote the exact relevant sentence

This allows users to verify information and builds trust in your responses.

Metadata-Aware Prompting

Advanced RAG systems retrieve not just content but metadata: document dates, authors, version numbers, confidence scores. Incorporate this into your prompts:

Retrieved sources with metadata:

[Confidence: 0.92, Date: 2024-03-15, Source: Release_Notes_v2.3.pdf] {content_1}

[Confidence: 0.76, Date: 2023-11-20, Source: Legacy_Documentation.pdf] {content_2}

Prioritize more recent and higher-confidence sources. If using older information, note that it may be outdated and suggest checking for newer documentation.

The Feedback Loop

RAG prompting improves through iteration. Monitor which queries produce poor results. Are users reformulating questions? Are responses citing irrelevant sources? Use this feedback to refine your query reformulation strategies, adjust grounding instructions, and improve context framing.

RAG transforms AI from a limited conversational tool into a powerful knowledge system grounded in your actual information. But retrieval alone isn't enough—thoughtful prompt design ensures that retrieved information translates into accurate, trustworthy, and useful responses.