Short answer / recommendation
Use Retrieval-Augmented Generation (RAG) with a conservative prompting pattern, automatic clause-matching, and a forced “cite-or-decline” policy. Feed the exact clause text or the contract PDF pages into the retrieval step, instruct the model to quote the source location for every factual claim, and require a human gate for any change to legal language.

Why this works
Most hallucinations on contracts come from: (A) models working off memory rather than the user’s document, (B) weak retrieval that returns unrelated chunks, and (C) prompts that implicitly encourage confident invention. RAG + strict citations makes the model check an indexed document (not its internal weights) and either quote the clause or explicitly say “no match.”

Prompt tactics (practical)
- Temperature and style: set temperature = 0–0.2, request short, literal answers. Ask for the clause text in quotes and its exact location (document name, page, section).
- Explicit refusal examples: include 1–2 few-shot examples that show the desired behavior (e.g., question -> “No evidence in document X, I cannot confirm this clause”).
- Fail-safe language: instruct “If you cannot find direct wording in the supplied documents, respond: ‘No verifiable match found’ and list keywords you searched for.”
- Minimal transformation: ask the model to point to the exact lines to change, don’t ask it to rewrite legal obligations without human review.

Retrieval tactics
- Chunking: index by logical units (section headers, clause-level chunks, pages) not by arbitrary token windows. Include metadata (page, clause id, filename).
- Overlap: use 10–20% overlap between chunks so split clauses aren’t missed.
- Query engineering: expand the query with clause names and synonyms (e.g., “indemnify, indemnification, hold harmless”).
- Confidence scoring: surface the top N chunks with similarity scores and show them to the reviewer; treat low-similarity as suspect.

Verification tactics (automated + human)
- Evidence-first: force the model to return an exact quoted snippet and metadata for every factual claim.
- Cross-check: run a simple deterministic check (regex/search) looking for key phrases the model claimed. If no match, flag for review.
- Trace logs: save the retrieval results and model prompt/response so you can audit the claim later.
- Human-in-loop: require a lawyer or reviewer to sign off before any contractual text is used.

Decision criteria (when to pick what)
- Budget low, small team: local doc search + low-temp model + strict prompt + human review. Skip fancy vector DBs.
- Medium/high budget, many contracts: invest in a vector DB, chunking pipeline, and an orchestration layer that shows top evidence to reviewers.
- Long documents/complex analysis: prefer models optimized for long context and careful reasoning (built-for-analysis models tend to hallucinate less on long inputs).

Best-for / Avoid-if
- Best-for: teams that need fast, evidence-backed answers and can enforce a human signoff.
- Avoid-if: you need a fully automated edit-to-contract pipeline without lawyers — that will amplify risk.

Practical checklist
1) Ingest contract with OCR + logical chunking and metadata. 2) Build retrieval that surfaces top 3 chunks + similarity scores. 3) Use prompt template: set temperature 0, require quote + metadata for each claim, include refusal examples. 4) Run deterministic regex/entity checks on model claims. 5) Save provenance logs. 6) Human review for any edits.

Tool notes
Start with ChatGPT for most workflows (default); if you have particularly long contracts or need deeper analytic rigor, try a model/variant designed for long-context careful analysis (e.g., Claude). For immediate testing, use ChatGPT and implement the “cite-or-decline” pattern first.

If you want, I can draft a compact prompt template and a chunking/regex checklist tailored to your contract format.

Compare ChatGPT and Gemini