feat: implement Context-Faithful Prompting — make LLMs use retrieved context (#667)

Resolves #667. Addresses R@5 vs E2E accuracy gap by forcing the
LLM to ground in retrieved context instead of parametric knowledge.

agent/context_faithful.py (293 lines):
- build_context_faithful_prompt(): context-before-question structure,
  explicit use-context instruction, I-dont-know escape hatch,
  passage numbering for citations, confidence calibration (1-5)
- build_summarization_prompt(): context-faithful version for session search
- build_answer_prompt(): context-faithful for direct Q&A
- assess_context_faithfulness(): heuristic faithfulness scoring
  (citation count, grounding ratio, honest-unknown detection)

tools/session_search_tool.py:
- Replaced hardcoded summarization prompt with build_summarization_prompt()
- LLM now forced to cite transcript passages and ground in context

tests/test_context_faithful_prompting.py (18 tests):
- Prompt structure, context-before-question, passage numbering
- Citation/confidence toggles, empty passages
- Summarization integration, answer generation
- Faithfulness assessment: citations, grounding ratio, honest unknown

agent/context_faithful.py

@@ -0,0 +1,293 @@
+"""Context-Faithful Prompting — Make LLMs Use Retrieved Context.
+
+Addresses the R@5 vs E2E accuracy gap by prompting the LLM to actually
+use the retrieved context instead of relying on parametric knowledge.
+
+Research: Context-faithful prompting achieves +5-15 E2E accuracy gains.
+
+Key patterns:
+1. Context-before-question structure (attention bias)
+2. Explicit "use the context" instruction
+3. Citation requirement (which passage used)
+4. Confidence calibration
+5. "I don't know" escape hatch
+
+Usage:
+    from agent.context_faithful import build_context_faithful_prompt
+    prompt = build_context_faithful_prompt(passages, query)
+"""
+
+from __future__ import annotations
+
+import os
+from typing import Any, Dict, List, Optional
+
+# Configuration
+CFAITHFUL_ENABLED = os.getenv("CFAITHFUL_ENABLED", "true").lower() not in ("false", "0", "no")
+CFAITHFUL_REQUIRE_CITATION = os.getenv("CFAITHFUL_REQUIRE_CITATION", "true").lower() not in ("false", "0", "no")
+CFAITHFUL_CONFIDENCE = os.getenv("CFAITHFUL_CONFIDENCE", "true").lower() not in ("false", "0", "no")
+CFAITHFUL_MAX_CONTEXT_CHARS = int(os.getenv("CFAITHFUL_MAX_CONTEXT_CHARS", "8000"))
+
+
+# ---------------------------------------------------------------------------
+# Prompt Templates
+# ---------------------------------------------------------------------------
+
+# Core instruction: forces the LLM to ground in context
+CONTEXT_FAITHFUL_INSTRUCTION = (
+    "You must answer based ONLY on the provided context below. "
+    "Do not use any prior knowledge or make assumptions beyond what is stated in the context. "
+    "If the context does not contain enough information to answer the question, "
+    "you MUST say: \"I don't know based on the provided context.\" "
+    "Do not guess. Do not fill in gaps with your training data."
+)
+
+# Citation instruction: forces the LLM to cite which passage it used
+CITATION_INSTRUCTION = (
+    "For each claim in your answer, cite the specific passage number "
+    "(e.g., [Passage 1], [Passage 3]) that supports it. "
+    "If you cannot cite a passage for a claim, do not include that claim."
+)
+
+# Confidence instruction: calibrates the LLM's certainty
+CONFIDENCE_INSTRUCTION = (
+    "After your answer, rate your confidence on a scale of 1-5:\n"
+    "1 = The context barely addresses the question\n"
+    "2 = Some relevant information but incomplete\n"
+    "3 = The context provides a partial answer\n"
+    "4 = The context provides a clear answer with minor gaps\n"
+    "5 = The context fully answers the question\n"
+    "Format: Confidence: N/5"
+)
+
+
+def build_context_faithful_prompt(
+    passages: List[Dict[str, Any]],
+    query: str,
+    require_citation: Optional[bool] = None,
+    include_confidence: Optional[bool] = None,
+    max_context_chars: int = CFAITHFUL_MAX_CONTEXT_CHARS,
+) -> Dict[str, str]:
+    """Build a context-faithful prompt with context-before-question structure.
+
+    Args:
+        passages: List of passage dicts with 'content' or 'text' key.
+            May have 'session_id', 'snippet', 'summary', etc.
+        query: The user's question.
+        require_citation: Override citation requirement.
+        include_confidence: Override confidence calibration.
+        max_context_chars: Max total context to include.
+
+    Returns:
+        Dict with 'system' and 'user' prompt strings.
+    """
+    if not CFAITHFUL_ENABLED:
+        return _fallback_prompt(passages, query)
+
+    if require_citation is None:
+        require_citation = CFAITHFUL_REQUIRE_CITATION
+    if include_confidence is None:
+        include_confidence = CFAITHFUL_CONFIDENCE
+
+    # Format passages with numbering for citation
+    context_block = _format_passages(passages, max_context_chars)
+
+    # Build system prompt
+    system_parts = [CONTEXT_FAITHFUL_INSTRUCTION]
+    if require_citation:
+        system_parts.append(CITATION_INSTRUCTION)
+    if include_confidence:
+        system_parts.append(CONFIDENCE_INSTRUCTION)
+
+    system_prompt = "\n\n".join(system_parts)
+
+    # Build user prompt: CONTEXT BEFORE QUESTION (attention bias)
+    user_prompt = (
+        f"CONTEXT:\n{context_block}\n\n"
+        f"---\n\n"
+        f"QUESTION: {query}\n\n"
+        f"Answer the question using ONLY the context above."
+    )
+
+    return {
+        "system": system_prompt,
+        "user": user_prompt,
+    }
+
+
+def _format_passages(
+    passages: List[Dict[str, Any]],
+    max_chars: int,
+) -> str:
+    """Format passages with numbering for citation reference."""
+    lines = []
+    total_chars = 0
+
+    for idx, passage in enumerate(passages, 1):
+        content = (
+            passage.get("content")
+            or passage.get("text")
+            or passage.get("snippet")
+            or passage.get("summary", "")
+        )
+        if not content:
+            continue
+
+        # Truncate individual passage if needed
+        remaining = max_chars - total_chars
+        if remaining <= 0:
+            break
+        if len(content) > remaining:
+            content = content[:remaining] + "..."
+
+        source = passage.get("session_id") or passage.get("source", "")
+        header = f"[Passage {idx}"
+        if source:
+            header += f" — {source}"
+        header += "]"
+
+        lines.append(f"{header}\n{content}\n")
+        total_chars += len(content)
+
+    if not lines:
+        return "[No relevant context found]"
+
+    return "\n".join(lines)
+
+
+def _fallback_prompt(
+    passages: List[Dict[str, Any]],
+    query: str,
+) -> Dict[str, str]:
+    """Simple prompt without context-faithful patterns (when disabled)."""
+    context = _format_passages(passages, CFAITHFUL_MAX_CONTEXT_CHARS)
+    return {
+        "system": "Answer the user's question based on the provided context.",
+        "user": f"Context:\n{context}\n\nQuestion: {query}",
+    }
+
+
+# ---------------------------------------------------------------------------
+# Summarization Integration
+# ---------------------------------------------------------------------------
+
+def build_summarization_prompt(
+    conversation_text: str,
+    query: str,
+    session_meta: Dict[str, Any],
+) -> Dict[str, str]:
+    """Build a context-faithful summarization prompt for session search.
+
+    This is designed to replace the existing _summarize_session prompt
+    in session_search_tool.py with a context-faithful version.
+    """
+    source = session_meta.get("source", "unknown")
+    started = session_meta.get("started_at", "unknown")
+
+    system = (
+        "You are reviewing a past conversation transcript. "
+        + CONTEXT_FAITHFUL_INSTRUCTION + "\n\n"
+        "Summarize the conversation with focus on the search topic. Include:\n"
+        "1. What the user asked about or wanted to accomplish\n"
+        "2. What actions were taken and what the outcomes were\n"
+        "3. Key decisions, solutions found, or conclusions reached\n"
+        "4. Specific commands, files, URLs, or technical details\n"
+        "5. Anything left unresolved\n\n"
+        "Cite specific parts of the transcript (e.g., 'In the conversation, the user...'). "
+        "If the transcript doesn't contain information relevant to the search topic, "
+        "say so explicitly rather than inventing details."
+    )
+
+    user = (
+        f"CONTEXT (conversation transcript):\n{conversation_text}\n\n"
+        f"---\n\n"
+        f"SEARCH TOPIC: {query}\n"
+        f"Session source: {source}\n"
+        f"Session date: {started}\n\n"
+        f"Summarize this conversation with focus on: {query}"
+    )
+
+    return {"system": system, "user": user}
+
+
+# ---------------------------------------------------------------------------
+# Answer Generation
+# ---------------------------------------------------------------------------
+
+def build_answer_prompt(
+    passages: List[Dict[str, Any]],
+    query: str,
+    conversation_context: Optional[str] = None,
+) -> Dict[str, str]:
+    """Build a context-faithful answer generation prompt.
+
+    For direct question answering (not summarization).
+    """
+    context_block = _format_passages(passages, CFAITHFUL_MAX_CONTEXT_CHARS)
+
+    system = "\n\n".join([
+        CONTEXT_FAITHFUL_INSTRUCTION,
+        CITATION_INSTRUCTION,
+        CONFIDENCE_INSTRUCTION,
+    ])
+
+    user_parts = []
+    user_parts.append(f"CONTEXT:\n{context_block}")
+
+    if conversation_context:
+        user_parts.append(f"RECENT CONVERSATION:\n{conversation_context[:2000]}")
+
+    user_parts.append(f"---\n\nQUESTION: {query}")
+    user_parts.append("\nAnswer based ONLY on the context above.")
+
+    return {
+        "system": system,
+        "user": "\n\n".join(user_parts),
+    }
+
+
+# ---------------------------------------------------------------------------
+# Quality Metrics
+# ---------------------------------------------------------------------------
+
+def assess_context_faithfulness(
+    answer: str,
+    passages: List[Dict[str, Any]],
+) -> Dict[str, Any]:
+    """Assess how faithfully an answer uses the provided context.
+
+    Heuristic analysis (no LLM call):
+    - Citation count: how many [Passage N] references
+    - Grounding ratio: answer terms present in context
+    - "I don't know" detection
+    """
+    if not answer:
+        return {"faithful": False, "reason": "empty_answer"}
+
+    answer_lower = answer.lower()
+
+    # Check for "I don't know" escape hatch
+    if "don't know" in answer_lower or "does not contain" in answer_lower:
+        return {"faithful": True, "reason": "honest_unknown", "citations": 0}
+
+    # Count citations
+    import re
+    citations = re.findall(r'\[Passage \d+\]', answer)
+    citation_count = len(citations)
+
+    # Grounding ratio: how many answer words appear in context
+    context_text = " ".join(
+        (p.get("content") or p.get("text") or p.get("snippet") or "").lower()
+        for p in passages
+    )
+    answer_words = set(answer_lower.split())
+    context_words = set(context_text.split())
+    overlap = len(answer_words & context_words)
+    grounding_ratio = overlap / len(answer_words) if answer_words else 0
+
+    return {
+        "faithful": grounding_ratio > 0.3 or citation_count > 0,
+        "citations": citation_count,
+        "grounding_ratio": round(grounding_ratio, 3),
+        "reason": "grounded" if grounding_ratio > 0.3 else "weak_grounding",
+    }

tests/test_context_faithful_prompting.py

@@ -0,0 +1,133 @@
+"""Tests for Context-Faithful Prompting — issue #667."""
+
+import pytest
+from agent.context_faithful import (
+    build_context_faithful_prompt,
+    build_summarization_prompt,
+    build_answer_prompt,
+    assess_context_faithfulness,
+    CONTEXT_FAITHFUL_INSTRUCTION,
+    CITATION_INSTRUCTION,
+    CONFIDENCE_INSTRUCTION,
+)
+
+
+class TestBuildContextFaithfulPrompt:
+    def test_returns_system_and_user(self):
+        passages = [{"content": "Paris is the capital of France.", "session_id": "s1"}]
+        result = build_context_faithful_prompt(passages, "What is the capital of France?")
+        assert "system" in result
+        assert "user" in result
+
+    def test_system_has_use_context_instruction(self):
+        passages = [{"content": "test content", "session_id": "s1"}]
+        result = build_context_faithful_prompt(passages, "test query")
+        assert "provided context" in result["system"].lower() or "context" in result["system"].lower()
+
+    def test_system_has_dont_know_escape(self):
+        passages = [{"content": "test", "session_id": "s1"}]
+        result = build_context_faithful_prompt(passages, "q")
+        assert "don't know" in result["system"].lower() or "I don't know" in result["system"]
+
+    def test_user_has_context_before_question(self):
+        passages = [{"content": "Test content here.", "session_id": "s1"}]
+        result = build_context_faithful_prompt(passages, "What is this?")
+        # Context should appear before the question
+        context_pos = result["user"].find("CONTEXT")
+        question_pos = result["user"].find("QUESTION")
+        assert context_pos < question_pos
+
+    def test_passages_are_numbered(self):
+        passages = [
+            {"content": "First passage.", "session_id": "s1"},
+            {"content": "Second passage.", "session_id": "s2"},
+        ]
+        result = build_context_faithful_prompt(passages, "q")
+        assert "Passage 1" in result["user"]
+        assert "Passage 2" in result["user"]
+
+    def test_citation_instruction_included_by_default(self):
+        passages = [{"content": "test", "session_id": "s1"}]
+        result = build_context_faithful_prompt(passages, "q")
+        assert "cite" in result["system"].lower() or "[Passage" in result["system"]
+
+    def test_confidence_calibration_included_by_default(self):
+        passages = [{"content": "test", "session_id": "s1"}]
+        result = build_context_faithful_prompt(passages, "q")
+        assert "confidence" in result["system"].lower() or "1-5" in result["system"]
+
+    def test_can_disable_citation(self):
+        passages = [{"content": "test", "session_id": "s1"}]
+        result = build_context_faithful_prompt(passages, "q", require_citation=False)
+        # Should not have citation instruction
+        assert "cite" not in result["system"].lower() or "citation" not in result["system"].lower()
+
+    def test_empty_passages_handled(self):
+        result = build_context_faithful_prompt([], "test query")
+        assert "system" in result
+        assert "user" in result
+
+
+class TestBuildSummarizationPrompt:
+    def test_includes_transcript(self):
+        prompts = build_summarization_prompt(
+            "User: Hello\nAssistant: Hi",
+            "greeting",
+            {"source": "cli", "started_at": "2024-01-01"},
+        )
+        assert "Hello" in prompts["user"]
+        assert "greeting" in prompts["user"]
+
+    def test_has_context_faithful_instruction(self):
+        prompts = build_summarization_prompt("text", "q", {})
+        assert "provided context" in prompts["system"].lower() or "context" in prompts["system"].lower()
+
+
+class TestBuildAnswerPrompt:
+    def test_returns_prompts(self):
+        passages = [{"content": "Answer is 42.", "session_id": "s1"}]
+        result = build_answer_prompt(passages, "What is the answer?")
+        assert "system" in result
+        assert "user" in result
+        assert "42" in result["user"]
+
+    def test_includes_conversation_context(self):
+        passages = [{"content": "info", "session_id": "s1"}]
+        result = build_answer_prompt(passages, "q", conversation_context="Previous message")
+        assert "Previous message" in result["user"]
+
+
+class TestAssessContextFaithfulness:
+    def test_empty_answer_not_faithful(self):
+        result = assess_context_faithfulness("", [])
+        assert result["faithful"] is False
+
+    def test_honest_unknown_is_faithful(self):
+        result = assess_context_faithfulness(
+            "I don't know based on the provided context.",
+            [{"content": "unrelated", "session_id": "s1"}],
+        )
+        assert result["faithful"] is True
+
+    def test_cited_answer_is_faithful(self):
+        result = assess_context_faithfulness(
+            "The capital is Paris [Passage 1].",
+            [{"content": "Paris is the capital.", "session_id": "s1"}],
+        )
+        assert result["faithful"] is True
+        assert result["citations"] >= 1
+
+    def test_grounded_answer_is_faithful(self):
+        result = assess_context_faithfulness(
+            "The system uses SQLite for storage with FTS5 indexing.",
+            [{"content": "The system uses SQLite for persistent storage with FTS5 indexing.", "session_id": "s1"}],
+        )
+        assert result["faithful"] is True
+        assert result["grounding_ratio"] > 0.3
+
+    def test_ungrounded_answer_not_faithful(self):
+        result = assess_context_faithfulness(
+            "The system uses PostgreSQL with MongoDB sharding.",
+            [{"content": "SQLite storage with FTS5.", "session_id": "s1"}],
+        )
+        assert result["grounding_ratio"] < 0.3

tools/session_search_tool.py

@@ -176,28 +176,11 @@ async def _summarize_session(
     conversation_text: str, query: str, session_meta: Dict[str, Any]
 ) -> Optional[str]:
     """Summarize a single session conversation focused on the search query."""
-    system_prompt = (
-        "You are reviewing a past conversation transcript to help recall what happened. "
-        "Summarize the conversation with a focus on the search topic. Include:\n"
-        "1. What the user asked about or wanted to accomplish\n"
-        "2. What actions were taken and what the outcomes were\n"
-        "3. Key decisions, solutions found, or conclusions reached\n"
-        "4. Any specific commands, files, URLs, or technical details that were important\n"
-        "5. Anything left unresolved or notable\n\n"
-        "Be thorough but concise. Preserve specific details (commands, paths, error messages) "
-        "that would be useful to recall. Write in past tense as a factual recap."
-    )
-
-    source = session_meta.get("source", "unknown")
-    started = _format_timestamp(session_meta.get("started_at"))
-
-    user_prompt = (
-        f"Search topic: {query}\n"
-        f"Session source: {source}\n"
-        f"Session date: {started}\n\n"
-        f"CONVERSATION TRANSCRIPT:\n{conversation_text}\n\n"
-        f"Summarize this conversation with focus on: {query}"
-    )
+    # Context-faithful prompting: force LLM to ground in transcript
+    from agent.context_faithful import build_summarization_prompt
+    prompts = build_summarization_prompt(conversation_text, query, session_meta)
+    system_prompt = prompts["system"]
+    user_prompt = prompts["user"]
 
     max_retries = 3
     for attempt in range(max_retries):