Short recommendation
Use a retrieval-augmented workflow + strict output format. Index your source documents (PDFs, papers, internal notes) into a searchable layer, feed the exact supporting snippets into the model’s context, and require each factual claim to include a precise citation and a quoted evidence snippet. For long briefs use Claude (or another long-context specialist) to keep sources in-window for analysis, then run automated cross-checks and a human spot-review pass.

Why this works (brief)
Hallucinations mainly come from: missing grounding, ambiguous prompts, and attempts to compress many facts without provenance. For long outputs, the single best fix is forcing provenance at the claim level and building a simple verification pipeline that flags claims with no or low-quality sources.

Decision criteria — pick the right approach based on constraints
- Budget: low — use search APIs + strict prompt templates that ask the model to cite URLs/snippets; medium/high — build a vector DB (Pinecone/FAISS) + RAG pipeline.
- Skill level: non-engineering teams — use Claude with detailed system prompts and manual source paste-in; engineering teams — automate retrieval and post-checking.
- Team size / QA needs: solo/small team — require 100% source-backed claims but do random human checks; large teams — add dual-review (writer + verifier) and automated cross-referencing.
- Output quality: high-stakes research requires explicit quoted evidence, document offsets, and human verification; low-stakes exploratory briefs can tolerate flagged speculative sections.

Concrete prompt pattern (use with Claude)
1) System: “You are an evidence-first research assistant. Do NOT assert facts without exact citations to supplied sources. Output must include: claim id, claim text, supporting source id(s), quoted snippet (<=200 chars), confidence (high/medium/low). If unsupported, label ‘unsupported’.”
2) User: “Here are sources [attach indexed snippets or paste text blocks]. Step A: Create an outline with which sources support each section. Step B: For each claim, list citation(s) and 1–2 supporting quotes. Step C: Produce the brief with inline [source-id] citations.”

Verification steps (automated + human)
- Automated: cross-check each citation against the original source text (string match of quote); flag mismatches; run web-search on claims missing sources.
- Heuristic checks: validate dates against timeline, check numeric ranges against primary data, verify named entities exist via reliable registries.
- Human: sample 10–20% of claims for deep-check; increase to 100% for high-stakes sections.

Best-for / Avoid-if
- Best-for: long, reference-heavy research briefs where provenance matters; teams needing reproducible claims.
- Avoid-if: quick brainstorming or narrative-first drafts where precise sourcing is not required.

Practical checklist (actionable)
- [ ] Build or designate a source store (documents + short extracted snippets).
- [ ] Create system prompt that forbids unsupported assertions and mandates claim-level citations.
- [ ] Use Claude (long-context) to keep sources in-window for each section.
- [ ] Require model to output claim IDs + quoted evidence + confidence.
- [ ] Run automated quote-to-source string-match; flag mismatches.
- [ ] Human-review sampled flagged claims; correct model prompt/data as needed.
- [ ] Track recurring hallucination patterns and adjust source coverage or prompt strictness.

If you want, I can draft a ready-to-paste system + user prompt for Claude and a JSON output schema for automated checking.

Compare Claude and ChatGPT