rag-search

name	rag-search
description	Azure AI Search knowledge base
enabled	true
index_name	knowledge-base

RAG Search Skill

Instructions

When the user asks about internal policies, documentation, procedures, or knowledge base content, use the rag_search tool to retrieve relevant content from the Azure AI Search knowledge base.

IMPORTANT: Always pass index_name: "knowledge-base" when calling rag_search. This is the dedicated search index for the generic knowledge base.

1. When to Use

Use this skill when the user's question relates to:

• Internal documentation, policies, or procedures
• Product or service information stored in the knowledge base
• Reference material, FAQs, or how-to guides
• Any question that should be grounded in authoritative internal content

Do NOT use for: real-time web data (→ web_search), calculations (→ code_interpreter).

2. Craft the Search Query

• Be specific with domain terms rather than generic phrases
• Include the key concept and context (e.g., "onboarding process new employee" rather than just "onboarding")
• If the first query returns low-relevance results, rephrase with synonyms or more specific terms

3. Interpret Search Results

Results contain: title, content, source, page, relevance score.

• Synthesize multiple results into a coherent answer rather than dumping raw excerpts
• Cite sources with document title and page number (e.g., "Per the Employee Handbook, p. 12...")
• Quote exact wording when precision matters (policies, procedures, requirements)
• Flag gaps — if the retrieved content doesn't fully answer the question, say so explicitly
• Note confidence — if results have low relevance scores, mention that the information may not be complete

4. Multi-Step Workflows

Combine RAG search with other skills for richer answers:

• Knowledge + summary: rag_search → document_summary to condense retrieved content
• Knowledge + analysis: rag_search → code_interpreter to analyze data referenced in docs
• Knowledge + web verification: rag_search → web_search to check if internal info is still current
• Knowledge + email: rag_search → email_draft to compose a response grounded in policy

5. Response Guidelines

• Present information clearly and accurately — do not paraphrase in ways that change the meaning
• Use bullet points or tables for structured information
• Always include the source document and page reference
• If multiple documents are relevant, present findings from each and note any differences
• If no relevant results are found, say so clearly and suggest the user refine their query

6. Error Handling

If the search returns an error or no results:

• Report the issue transparently to the user
• Suggest alternative search terms
• Offer to try web_search as a fallback if appropriate