This Claude skill serves as the critical final stage in a Retrieval-Augmented Generation (RAG) pipeline, specializing in synthesizing information from multiple retrieved documents into coherent, cited answers. By prioritizing the original user query and providing automated source attribution, it ensures high-fidelity responses while implementing robust fallback logic to prevent hallucinations when no relevant context is found.

When should I use generation?

generation is useful in the following scenarios: • RAG Pipeline Integration: Acting as the definitive synthesis step to transform raw document snippets into polished, citation-backed responses for AI agents. • Enterprise Knowledge Retrieval: Aggregating data from internal wikis and documentation to provide employees with accurate, source-verified answers to complex queries. • Automated Customer Support: Powering help centers by generating precise answers directly from knowledge base articles with transparent source metadata. • Hallucination Prevention: Implementing intelligent fallback mechanisms that acknowledge information gaps rather than generating inaccurate content when context is missing.

name	generation
description	Generates answers by synthesizing information from retrieved documents with citations. Use as final RAG pipeline step, when creating answers from multiple sources, or when the user mentions answer generation or synthesis.

Answer Generation

Instructions

Generate answers using functions in components/generator.py. Synthesizes information from graded-relevant documents or provides fallback when no context available.

Default workflow:

# Use original query (not rewritten) for generation
result = generate_answer_with_metadata(original_query, relevant_documents)
answer = result['answer']

Key functions:

# Standard generation with metadata (preferred)
result = generate_answer_with_metadata(query, documents, include_sources=True)
# Returns: answer, num_documents_used, has_context, sources, collections_used

# Basic generation
result = generate_answer(query, documents)
# Returns: answer, num_documents_used, has_context, model_used

Generation modes:

With context (documents provided): Synthesizes from multiple documents with source attribution
Without context (no documents or empty list): Acknowledges lack of information, provides helpful fallback

Critical: Always use original_query for generation, NOT current_query (which may be rewritten). This ensures the answer addresses what the user actually asked.

Implementation: components/generator.py, uses GENERATION_MODEL from config.py (default: Haiku 4.5), temperature 0.3, max tokens 2000.

Examples

Example 1: Standard generation with context

# Input
result = generate_answer_with_metadata(
    "What gaming laptops do you have?",
    relevant_documents  # 3 documents from catalog
)

# Output
{
    "answer": "We have several gaming laptops including TechBook Pro 15 with RTX 4060...",
    "num_documents_used": 3,
    "has_context": True,
    "sources": ["techmart_catalog.csv"],
    "collections_used": ["catalog"]
}

Example 2: Fallback (no documents)

# Input
result = generate_answer("obscure query", [])

# Output
{
    "answer": "I don't have relevant information about this in our knowledge base...",
    "num_documents_used": 0,
    "has_context": False,
    "sources": [],
    "collections_used": []
}

Example 3: Pipeline integration

# After retrieval, grading, retries
if relevant_documents:
    # Generate with context
    generation_result = generate_answer_with_metadata(
        original_query,  # NOT current_query
        relevant_documents
    )
    print(f"✓ Answer from {generation_result['num_documents_used']} docs")
else:
    # Fallback after exhausting retries
    generation_result = generate_answer(original_query, [])
    print("⚠ Fallback answer (no relevant docs)")

print(generation_result['answer'])

Example 4: Using original vs rewritten query

# Setup
original_query = "gaming laptops"
current_query = "high performance gaming laptop computers RTX graphics"  # Rewritten

# After retrieval with current_query
relevant_docs = filter_relevant_documents(graded_docs)

# Generate with ORIGINAL query
result = generate_answer(original_query, relevant_docs)  # ✓ Correct
# NOT: generate_answer(current_query, relevant_docs)     # ✗ Wrong

generation

When & Why to Use This Skill

Use Cases