Grounding LLM Responses

What is Grounding? Grounding ensures LLM outputs are based on reliable sources rather than model parameters alone. It bridges the gap between fluent generation and factual accuracy.

Grounding Techniques

Document Grounding (RAG) Base responses on retrieved documents:

def document_grounded(query: str) -> str:
    docs = vector_store.search(query, k=5)
    context = "
".join([d.text for d in docs])

    return llm.generate(f"""
You are a helpful assistant. Answer based ONLY on the provided context.
If the context does not contain the answer, say so.

Context:
{context}

Question: {query}
Answer:
    """)

API Grounding Ground in real-time data:

def api_grounded(query: str) -> str:
    # Extract entities
    entities = extract_entities(query)

    # Fetch real data
    data = {}
    for entity in entities:
        data[entity] = api.lookup(entity)

    return llm.generate(f"""
Use ONLY this data to answer:
{json.dumps(data)}

Question: {query}
    """)

Code Execution Grounding Ground calculations in actual execution:

def code_grounded(query: str) -> str:
    # Generate code
    code = llm.generate(f"Write Python code to answer: {query}")

    # Execute
    result = execute_safely(code)

    # Generate response with result
    return llm.generate(f"""
The code executed and produced: {result}
Explain this result for: {query}
    """)

Grounding vs No Grounding

Grounding Sources

Grounding Prompts

# Strict grounding
Answer using ONLY the provided context. Do not use prior knowledge.
If unsure, state you cannot answer from the given context.

# Soft grounding
Use the provided context as your primary source.
Supplement with your knowledge only when context is insufficient.
Clearly distinguish between sourced and unsourced information.

Verification Always verify grounded responses:

• Check citations match source content
• Test with known-answer queries
• Monitor user feedback on accuracy