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+# Section 3 — Technical Approach
+Owner: Muhammad Maaz — Research/Implementation Engineer
+
+## 3.1 Shared Foundation: Embedding Model
+
+All four criteria that depend on semantic comparison (factual_grounding, relevance,
+completeness) share a single embedding backbone: sentence transformers'
+all-MiniLM-L6-v2. This is loaded once per process via a singleton pattern, not
+once per evaluate() call.
+
+Manual testing (see appendix) confirms load time is trivial on CPU after the
+initial ~80MB download, but repeated re-instantiation of SentenceTransformer()
+inside a hot loop is the single biggest performance risk in this library, since
+model loading dominates over the actual embedding computation. The singleton
+must also be safe to call from a multi-threaded context (e.g. a Flask service
+evaluating several responses concurrently) first-load race conditions are a
+known failure mode for naive singletons and will be covered explicitly in the
+Integration Engineer's test suite.
+
+## 3.2 factual_grounding
+
+**Method:** Sentence level cosine similarity between the response and each
+sentence in the provided context, taking the maximum (not the average) as the
+grounding score. Splitting context into sentences and taking the max avoids
+diluting the signal when only one sentence in a long context actually supports
+the response averaging across unrelated sentences would wash out a strong
+match.
+
+**Verified limitation (from manual testing):** Cosine similarity measures
+semantic/topical closeness, not factual correctness. A paraphrased, accurate
+response scored 0.982 against its context. A response that directly
+contradicted its context (stating "50 degrees Celsius" instead of the correct
+"100 degrees Celsius" for water's boiling point) still scored 0.822 — high
+enough to risk a false "grounded" verdict under a naive fixed threshold (e.g.
+"score > 0.7 = grounded").
+
+**Implication for design:** factual_grounding cannot rely on similarity alone
+for numerical or entity-specific claims. v1 scope will use similarity as the
+primary signal, but will flag (not silently pass) any response containing a
+number that doesn't literally appear in the matched context sentence, as a
+supplementary check. This is a heuristic, not full fact-checking genuinely
+out of scope for v1 is anything requiring external knowledge verification
+(e.g. checking claims against the internet).
+
+**In scope:** context-grounded factual claims, numeric mismatches against
+provided context.
+**Out of scope for v1:** claims not covered by the given context at all,
+world-knowledge fact-checking, multi-hop reasoning across multiple context
+sentences.
+
+## 3.3 relevance
+
+**Method:** Cosine similarity between the prompt embedding and the response
+embedding directly no context needed for this check, since relevance is
+about whether the response addresses the prompt, not whether it's grounded
+in supporting material.
+
+**Known risk:** A response can be topically related to the prompt without
+actually answering it (e.g. restating the question, or answering a related-
+but-different question). Pure embedding similarity will score these as
+falsely relevant. v1 will document this as a known limitation rather than
+attempt to solve it with a single-model approach — flagged in Section 6
+(Known Risks).
+
+## 3.4 refusal_check
+
+**Method:** Primarily pattern based, not embedding-based, since refusals have
+recognizable lexical structure but with a specific, named failure mode from
+the project brief: phrases like "I cannot stress enough how important this
+is" contain refusal-adjacent language ("I cannot") without being a refusal at
+all.
+
+**Design decision:** A naive keyword match on "I cannot" / "I won't" /
+"I'm unable to" is not sufficient. The check needs at minimum:
+1. Keyword/phrase matching for common refusal openers, restricted to sentence-
+   initial position (refusals almost always start a response, whereas
+   "I cannot stress enough" typically doesn't open the response).
+2. A secondary check: does the response, excluding the flagged phrase, still
+   contain a substantive answer to the prompt? If yes, it's not a refusal
+   regardless of the opening phrase.
+
+This is the highest risk criterion for false positives and will need the
+largest fixture set in the Integration Engineer's test suite.
+
+## 3.5 completeness
+
+**Method:** Sentence level coverage decompose the prompt into implied
+sub-aspects (naive approach: treat clauses/questions within the prompt as
+separate aspects) and check whether each has at least one semantically
+similar sentence in the response, using the same embedding approach as
+factual_grounding.
+
+**Open question carried into Section 6:** decomposing a prompt into
+"aspects" reliably is the least well defined part of this criterion. v1 will
+likely use a simple heuristic (splitting on conjunctions and question marks)
+rather than anything more sophisticated, and this will be explicitly flagged
+as a limitation rather than solved perfectly in v1.
+
+## Appendix: Manual Verification Data
+
+| Case | Context | Response | Cosine Similarity |
+|------|---------|----------|-------------------|
+| 1 (paraphrase, accurate) | "Paris is the capital of France." | "The capital of France is Paris." | 0.982 |
+| 2 (contradiction) | "Water boils at 100 degrees Celsius at sea level." | "Water boils at 50 degrees Celsius." | 0.822 |
+
+Test environment: Python venv, sentence-transformers 5.6.0, all-MiniLM-L6-v2,
+CPU-only, Windows 11.