Enhancing RAG: The Critical Role of Context Sufficiency

Author(s): Alok Ranjan Singh

Originally published on Towards AI.

RAG (Retrieval-Augmented Generation) is one of the most exciting ways to make language models more knowledgeable, but relevance alone isn’t enough. Many developers and researchers assume that if a document is relevant, the model will answer correctly. That’s a trap. What actually matters is context sufficiency — whether the retrieved information contains all the facts needed to answer the question accurately.

In this article, we’ll go deep into the concept, explain why it matters, how to measure it, and how to improve RAG systems for robust knowledge-intensive applications.

1️⃣ Why Relevance ≠ Sufficiency

Imagine asking:
“Who invented the transistor and in which year?”

A standard RAG system might retrieve a paragraph about transistors in general — but miss the inventors or the year. A naive LLM might hallucinate:

“The transistor was invented by Gordon Moore in 1965.” ❌

Even though the retrieved documents were “relevant,” they were insufficient.
✅ Key takeaway: relevance is not enough; you need sufficient context.

2️⃣ Measuring Context Sufficiency

Context sufficiency is about verifying that the retrieved documents contain enough information to answer the query correctly.

Approaches include:

• Keyword / Entity Coverage: Check if all critical entities (people, dates, places) from the question exist in the retrieved text.
• Semantic Sufficiency: Measure how semantically close the retrieved context is to the query using embeddings and cosine similarity.
• Threshold-based Scoring: Compute a sufficiency score (e.g., 0–1). If below a threshold, trigger additional retrieval.

The smarter the sufficiency check, the less the model hallucinates.

3️⃣ Improving Context Sufficiency

Dynamic Retrieval: If the context is insufficient, fetch additional documents or re-rank the results.

Query Refinement / Decomposition: Break complex queries into sub-questions to ensure all facts are covered.

Selective Generation / Abstention: Train or prompt your model to abstain from answering if the context isn’t sufficient, reducing incorrect outputs.

Studies show selective generation with sufficiency checks can improve the fraction of correct answers by 2–10% on complex fact-rich queries (arXiv 2024).

4️⃣ Practical Workflow

1. Retrieve relevant documents using FAISS or another vector store.
2. Evaluate Sufficiency using embeddings and semantic similarity or entity presence.
3. Generate Answer only if context is sufficient; otherwise, dynamically fetch more.

This workflow ensures that RAG models generate reliable answers even in multi-entity or knowledge-intensive domains like scientific papers, legal text, or finance.

5️⃣ Why it Matters

Without context sufficiency:

• Models hallucinate
• Multi-entity queries fail
• Answers may be partially correct but misleading

With context sufficiency:

• Reduced hallucinations
• Higher accuracy in fact-rich queries
• Reliable output for high-stakes applications

This chart is the evidence: when contexts are labeled sufficient, the correctness of model answers rises sharply and hallucination rates fall. Conversely, under “relevant but insufficient” contexts, models — even very capable ones — produce more incorrect or invented answers. A few deeper implications:

1. Metrics need stratification. Overall accuracy numbers hide the truth. If you evaluate RAG systems only with aggregate accuracy, you don’t see whether errors arise from retrieval insufficiency or model reasoning failure. Stratifying errors by sufficiency reveals where to invest engineering effort (retrieval vs. model).
2. AutoRaters / LLM judges are practical. Google’s AutoRater idea — using an LLM to classify sufficiency — works well in practice and is easier to deploy than human-in-the-loop checks. Use it to triage queries: if AutoRater says “insufficient,” don’t generate; instead, re-retrieve or decompose.
3. Sufficiency thresholds are tunable. Different applications have different risk tolerances. Set the sufficiency threshold higher for legal/medical tasks and lower for casual QA.

📜 Full Code Walkthrough

• RAG Architecture: Input → Retrieval → Context → LLM → Answer
• Context Sufficiency Check: Query + Retrieved Docs → Evaluate → Accept / Expand
• Dynamic Retrieval Loop: Iteratively fetch more context until threshold met

# ----------------------------
# Step 0: Install dependencies
# ----------------------------
!pip install -q faiss-cpu sentence-transformers transformers

# ----------------------------
# Step 1: Imports & Logging Setup
# ----------------------------
import warnings, os, logging
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import faiss
import torch
from transformers import pipeline, set_seed

# Suppress warnings and verbose logs
warnings.filterwarnings("ignore")
os.environ["TRANSFORMERS_VERBOSITY"] = "error"
logging.getLogger("transformers").setLevel(logging.ERROR)

# ============================================================
# Step 2: LLM Answer Generation
# ============================================================
def generate_llm_answer(
 query: str,
 context: str,
 model_name: str = "gpt2-large",
 max_new_tokens: int = 20,
 dtype: torch.dtype = None
):
 """
 Generate an answer from an LLM using a retrieval-augmented context.

 Parameters
 ----------
 query : str
 The user question.
 context : str
 Retrieved textual context to guide the LLM.
 model_name : str
 Name of the Hugging Face model used.
 max_new_tokens : int
 Number of new tokens to generate.
 dtype : torch.dtype, optional
 Data type for model inference.

 Returns
 -------
 answer : str
 Model-generated answer.
 """
 print("\n🧠 Generating LLM answer...")
 prompt = (
 "Answer the question using ONLY the context below. "
 "If the answer is not in the context, say 'Not enough information'.\n\n"
 f"Context:\n{context}\n\nQuestion: {query}\nAnswer:"
 )

 generator = pipeline(
 "text-generation",
 model=model_name,
 tokenizer=model_name,
 device=0 if torch.cuda.is_available() else -1,
 truncation=True,
 dtype=dtype,
 )

 # Ensuring deterministic generation
 set_seed(42)

 # Fallback for padding token warnings
 if generator.model.config.pad_token_id is None:
 generator.model.config.pad_token_id = generator.model.config.eos_token_id

 output = generator(
 prompt,
 max_new_tokens=max_new_tokens,
 num_return_sequences=1,
 do_sample=False, # deterministic behavior
 )[0]["generated_text"]

 answer = output.split("Answer:")[-1].strip()
 return answer

# ============================================================
# Step 3: Corpus Setup
# ============================================================
print("\nSetting up knowledge corpus...")

corpus = [
 "Transistors are widely used in electronics for amplification and switching.",
 "Semiconductor devices like diodes and transistors form the backbone of modern computers.",
 "Vacuum tubes were used in early computers before transistors were invented.",
 "Transistors revolutionized electronic circuits and enabled more compact devices.",
 "Semiconductors are essential for CPUs, memory chips, and modern computing devices.",
 "Moore's law predicts the number of transistors on a chip doubles roughly every two years.",
 "Electronic engineers design circuits with transistors to manage current flow.",
 "Amplifiers use transistors to strengthen audio and radio signals.",
 "Switching circuits in digital electronics rely heavily on transistor technology.",
 "Bipolar junction transistors and field-effect transistors are common types of transistors.",
 # Distractors
 "John von Neumann contributed to computer architecture.",
 "The first microprocessor was created in the 1970s.",
 "Transistor radios became popular in the 1950s."
]

# Encode the corpus with Sentence-BERT
embedder = SentenceTransformer("all-MiniLM-L6-v2")
corpus_embeddings = embedder.encode(corpus, convert_to_numpy=True)

# Build a FAISS index for similarity search
dimension = corpus_embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(corpus_embeddings)

# ============================================================
# Step 4: Retrieval Functions
# ============================================================
def retrieve_documents(query, k=3, dynamic_corpus=None):
 """
 Retrieve top-k documents from a (dynamic) corpus using FAISS similarity search.
 """
 print("📖 Retrieving documents...")
 source = dynamic_corpus if dynamic_corpus is not None else corpus
 embeddings = embedder.encode(source, convert_to_numpy=True)
 index_temp = faiss.IndexFlatL2(embeddings.shape[1])
 index_temp.add(embeddings)
 query_emb = embedder.encode([query], convert_to_numpy=True)
 distances, indices = index_temp.search(query_emb, k)
 return [source[i] for i in indices[0]]

# ============================================================
# Step 5: Semantic Sufficiency Evaluation
# ============================================================
def evaluate_semantic_sufficiency(query, retrieved_docs):
 """
 Compute a semantic sufficiency score between query and retrieved documents.

 A high score means that the retrieved context is semantically close
 to the query, implying sufficient context coverage.
 """
 print("🧩 Evaluating semantic sufficiency...")
 query_emb = embedder.encode([query])
 doc_embs = embedder.encode(retrieved_docs)
 similarities = cosine_similarity(query_emb, doc_embs)
 return float(similarities.mean())


def is_sufficient(query, retrieved_docs, threshold=0.7):
 """
 Decide if the retrieved context is sufficient to answer the query.
 """
 score = evaluate_semantic_sufficiency(query, retrieved_docs)
 print(f"Sufficiency Score = {score:.3f} → {'✅ Sufficient' if score >= threshold else '❌ Insufficient'}")
 return score >= threshold

# ============================================================
# Step 6: RAG Workflow with Sufficiency Check
# ============================================================
def rag_with_context_sufficiency(query, k=3, max_attempts=3, threshold=0.6):
 """
 Demonstrate RAG pipeline that detects context insufficiency and refines retrieval.
 """
 print("\n🚀 Starting RAG demo with Context Sufficiency...\n")
 dynamic_corpus = corpus.copy()
 attempts = 1
 final_answer = None

 while attempts <= max_attempts:
 print(f"\n============================\n🔁 ATTEMPT {attempts}\n============================")
 retrieved_docs = retrieve_documents(query, k=min(len(dynamic_corpus), k + attempts), dynamic_corpus=dynamic_corpus)
 context = "\n".join(retrieved_docs)

 # Check context sufficiency
 sufficient = is_sufficient(query, retrieved_docs, threshold)

 print(f"\n📚 Retrieved Context:\n{'-'*60}\n{context}\n{'-'*60}")
 final_answer = generate_llm_answer(query, context)
 print(f"\n🗨️ LLM Answer (Attempt {attempts}):\n{'-'*60}\n{final_answer}\n{'-'*60}")

 if sufficient:
 print("\n✅ Context sufficient — stopping retrieval loop.")
 break

 print("\n⚠️ Context insufficient — expanding corpus for better recall...")
 new_doc = "John Bardeen, Walter Brattain, and William Shockley invented the transistor in 1947."
 if new_doc not in dynamic_corpus:
 dynamic_corpus.append(new_doc)
 attempts += 1

 return retrieved_docs, final_answer

# ============================================================
# Step 7: Run Example Query
# ============================================================
query = "Who invented the transistor and when?"
retrieved_docs, answer = rag_with_context_sufficiency(query)
print("\n🎯 Final Answer:", answer)

🚀 Starting RAG demo with Context Sufficiency...


============================
🔁 ATTEMPT 1
============================
📖 Retrieving documents...
🧩 Evaluating semantic sufficiency...
Sufficiency Score = 0.578 → ❌ Insufficient

📚 Retrieved Context:
------------------------------------------------------------
Transistor radios became popular in the 1950s.
Transistors revolutionized electronic circuits and enabled more compact devices.
Transistors are widely used in electronics for amplification and switching.
Vacuum tubes were used in early computers before transistors were invented.
------------------------------------------------------------

🧠 Generating LLM answer...

🗨️ LLM Answer (Attempt 1):
------------------------------------------------------------
The transistor was invented by Gordon Moore in 1965.

Moore's Law states that the
------------------------------------------------------------

⚠️ Context insufficient — expanding corpus for better recall...

============================
🔁 ATTEMPT 2
============================
📖 Retrieving documents...
🧩 Evaluating semantic sufficiency...
Sufficiency Score = 0.611 → ✅ Sufficient

📚 Retrieved Context:
------------------------------------------------------------
John Bardeen, Walter Brattain, and William Shockley invented the transistor in 1947.
Transistor radios became popular in the 1950s.
Transistors revolutionized electronic circuits and enabled more compact devices.
Transistors are widely used in electronics for amplification and switching.
Vacuum tubes were used in early computers before transistors were invented.
------------------------------------------------------------

🧠 Generating LLM answer...

🗨️ LLM Answer (Attempt 2):
------------------------------------------------------------
The transistor was invented by Walter Brattain and William Shockley in 1947.
------------------------------------------------------------

✅ Context sufficient — stopping retrieval loop.

🎯 Final Answer: The transistor was invented by Walter Brattain and William Shockley in 1947.

7️⃣ Resources

• Research Paper: Sufficient Context in RAG (arXiv 2024)
• Google Research Blog: RAG with Sufficient Context

Conclusion
Context sufficiency is the missing ingredient in most RAG systems. By carefully measuring and improving the completeness of retrieved information, we can significantly reduce hallucinations and boost answer accuracy.

💡 If you’re building AI applications that rely on accurate factual responses, sufficiency-aware RAG is not optional — it’s essential.

🧑‍💻 Author

Alok Ranjan Singh — AI Engineer & Researcher | Exploring LLMs, RAG Systems, and Context-Sufficient Architectures
Building practical systems that make large language models more grounded, interpretable, and reliable.

🔗 LinkedIn Profile
📂 GitHub Repository
🧠 Other Writings: Medium Profile

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Published via Towards AI