Domain Templates

Each template specifies: corpus shape, chunking, embedding model, metadata schema, retrieval strategy, guardrails, eval criteria.

Template 1: Customer Support

Corpus Shape

• Help articles (500-2000 words), macros, product FAQs, historical tickets, release notes.
• High churn: content changes weekly.
• Often multilingual.

Chunking

• Help articles: Markdown header split, 800 chars, 150 overlap.
• Historical tickets: per-turn; one chunk = (question + resolution) pair.
• Release notes: per version, fixed-size 600 chars.

Embedding

• text-embedding-3-small (cost-sensitive) or multilingual embed-multilingual-v3.0 (Cohere) if non-English.

Metadata Schema

{
  "article_id": str, "category": str, "product": str,
  "locale": str, "audience": Literal["customer","agent","internal"],
  "updated_at": datetime, "deprecated": bool,
  "resolution_success_rate": float  # for ticket chunks
}

Retrieval

• Hybrid BM25 + dense; reranker.
• Hard filter: deprecated=false, audience in allowed_audiences, locale in user_locales.
• Soft bias: recency decay (half-life 180 days); boost resolution_success_rate for ticket chunks.

Guardrails

• Hallucination tolerance: low. Answer grounded strictly in retrieved content; fall back to "contact support" if confidence low.
• PII: strip customer names/emails from historical tickets before indexing.
• Response style: short, action-oriented, always cite article.

Eval

• Resolution rate (did the user stop asking?).
• Deflection rate (% of queries handled without human).
• Agent override rate (agent edits response before sending).
• First-contact resolution within N turns.

Template 2: Developer Documentation

Corpus Shape

• API docs (generated), tutorials, cookbook examples, SDK source code, GitHub issues.
• Heterogeneous: prose + code + reference tables.

Chunking

• Prose: Markdown header split, 1000 chars, 200 overlap.
• Code: Language-aware splitter, 1500 chars; preserve whole functions/classes.
• API reference: one chunk per endpoint or method.

Embedding

• voyage-code-3 for code-heavy; text-embedding-3-large for mixed.
• Separate indexes for code vs prose; route per query intent.

Metadata Schema

{
  "doc_id": str, "kind": Literal["prose","code","api","example"],
  "language": str, "library": str, "version": str,
  "section_path": list[str], "symbol": str | None,
  "updated_at": datetime
}

Retrieval

• Query classifier: "how do I" -> prose+examples; "what does X return" -> api; "why does X fail" -> issues+examples.
• Version-aware filter from user's installed version.
• Code-aware reranker (cross-encoder trained on code-query pairs).

Guardrails

• Answer must include a runnable code snippet when a how-to is requested.
• Version lock: never return docs for a different major version unless explicitly asked.
• Hallucination tolerance: zero on API signatures. Verify symbol exists in indexed API reference before emitting.

Eval

• Code snippet runnability (execute in sandbox; pass/fail).
• API signature accuracy (does returned signature match indexed reference?).
• Link coverage (% answers that cite the canonical doc page).
• Offline benchmark: SWE-bench-retrieval, CodeRAG-bench.

Template 3: Legal

Corpus Shape

• Contracts, case law, statutes, internal memos, regulations.
• Stable content; layered (statute -> regulation -> case interpretation).
• Extreme precision needs; jurisdictional.

Chunking

• Contracts / statutes: section/clause aware; proposition-based for atomic facts.
• Case law: head-note + holding + reasoning separately indexed.
• Preserve section numbering and hierarchy in metadata.

Embedding

• Domain-tuned: Nomic Embed Legal, Voyage voyage-law-2, or fine-tune on case retrieval corpus.

Metadata Schema

{
  "doc_id": str, "kind": Literal["contract","statute","regulation","case","memo"],
  "jurisdiction": str, "effective_date": date, "superseded_by": str | None,
  "section_path": list[str], "clause_number": str | None,
  "citation": str, "parties": list[str] | None,
  "valid_from": date | None, "valid_to": date | None,
  "authority_score": float
}

Retrieval

• Hybrid search mandatory; BM25 for exact statute lookups (e.g., "Section 11 USC 362").
• Strict filters: jurisdiction, effective_date <= query_date, superseded_by is null.
• Time-travel support (valid_at) for historical analysis.
• Hierarchical: first retrieve statute, then regulations, then cases interpreting it.

Guardrails

• Citations mandatory; every claim attributed.
• Never paraphrase statute wording; quote verbatim.
• "This is not legal advice" disclaimer.
• Superseded content filter at query time, with explicit override.
• PII: redact clients/parties if showing to other users.

Eval

• Citation correctness (cited source actually supports claim) — human-graded.
• Jurisdictional correctness.
• Temporal correctness (statute version active at query date).
• Attorney review score on a sample.

Template 4: Medical

Corpus Shape

• Clinical guidelines, drug monographs, clinical trials, EHR notes, patient education.
• Very high stakes; life-safety.
• Regulated: HIPAA in US, GDPR in EU.

Chunking

• Guidelines: propositional (each recommendation a chunk).
• Drug monographs: section-aware (indications, dosing, contraindications).
• Clinical trials: structured abstracts, one chunk per PICO field.

Embedding

• BioBERT, PubMedBERT, or Nomic Embed Medical. Fine-tune on UMLS synonyms.

Metadata Schema

{
  "doc_id": str, "kind": Literal["guideline","drug","trial","ehr","patient_ed"],
  "icd10": list[str], "snomed_ct": list[str], "rxnorm": list[str],
  "evidence_level": Literal["A","B","C","D"],
  "population": dict,          # age range, pregnancy, renal function
  "updated_at": datetime, "authority": str,
  "contraindications": list[str],
  "phi_status": Literal["none","de-identified","identified"]
}

Retrieval

• Query augmentation with medical ontology expansion (UMLS/SNOMED synonyms).
• Strict filter on authority for clinical-decision queries (e.g., only from approved guideline sources).
• Boost higher evidence levels.
• Population filter: match patient demographics explicitly.

Guardrails

• HIPAA: no PHI leaves the secure boundary; all operations logged.
• Answer template: always include evidence level, source, caveats.
• Hard refusals: dosing recommendations without patient context, diagnosis, emergency.
• Minimum authority threshold per query type (e.g., must be from NIH/guideline source).
• Version-pinned: drug monographs versioned; use latest unless queried historically.
• Disclaimer: "for informational purposes; not a substitute for clinician judgment".
• Audit log: every query + retrieval + response stored for compliance.

Eval

• Clinical accuracy (clinician-graded).
• Safety (any contraindication missed = fail).
• Citation match (cited source supports claim).
• Population-appropriate (pediatric vs adult dosing correct).
• Benchmark: MedQA, MedMCQA with retrieval-augmented variants.

Template 5: Financial

Corpus Shape

• 10-K/10-Q filings, earnings transcripts, analyst research, market data, internal research.
• Structured (tables, XBRL) + unstructured (MD&A narrative).
• Time-sensitive; regulatory.

Chunking

• 10-K/10-Q: section-aware (Item 1, 1A Risk Factors, 7 MD&A).
• Earnings transcripts: per speaker + topic shift (semantic chunking).
• Tables: preserve as structured data; NL2SQL hybrid (see tabular-rag).
• Research reports: executive summary + sections.

Embedding

• voyage-finance-2, FinE5, or FinBERT fine-tuned.

Metadata Schema

{
  "doc_id": str, "kind": Literal["10k","10q","transcript","research","news","table"],
  "ticker": str, "fiscal_period": str,        # "2025-Q3"
  "filing_date": date, "reporting_date": date,
  "section": str, "speaker": str | None,
  "currency": str, "is_restatement": bool,
  "embargo_until": datetime | None            # for non-public research
}

Retrieval

• Ticker filter mandatory for company-specific queries.
• Fiscal period filter or time-aware scoring.
• Restatement handling: when is_restatement=true, flag prior versions as superseded.
• Embargo: respect embargo_until; filter at query time.
• Hybrid structured (numbers from tables) + unstructured (narrative) retrieval.

Guardrails

• Forward-looking statements flagged per Safe Harbor.
• No advice on buy/sell/hold — information only.
• Material non-public information: restrict by user entitlement.
• Numerical accuracy: extract numbers from the source (not paraphrased).
• Citation with page + paragraph precision.
• Audit: every research query logged per regulatory requirements (FINRA).

Eval

• Numerical extraction accuracy (reported numbers match filing exactly).
• Temporal correctness (did the query find the right fiscal period?).
• Forward-looking statement detection rate.
• Analyst QA score on a monthly sample.
• Embargo leak rate (should be zero).

Cross-Domain Patterns

Anti-Patterns (Cross-Domain)

Production Checklist (Per-Domain)

• Corpus shape characterized (count, size, churn rate, language)
• Chunking strategy picked per content type in the corpus
• Domain-tuned embedding model evaluated vs general-purpose
• Metadata schema declared and validated (Pydantic)
• Authority signal defined and populated
• Time / version filter wired
• Ontology augmentation (if applicable)
• Guardrail prompt templates checked by a domain expert
• Citation format enforced in the response schema
• Domain-specific eval set (>= 100 queries, expert-graded)
• Regulatory audit logging turned on where required
• Refusal criteria documented and tested
• Escalation path (human review) for low-confidence answers
• Re-evaluation cadence scheduled (quarterly for regulated domains)