[Steve Side Hustler]

Hi everyone — I’m exploring simple, practical ways to use RAG (retrieval-augmented generation) to help colleagues find answers in our internal documents. I’m not a developer, so I’m looking for clear, non-technical guidance and real-world tips.

Specifically, I’d love advice on these points:

• Document preparation: How should we split and tag documents (chunk size, metadata)?
• Search and relevance: Best ways to make retrieved content reliable and up-to-date?
• Model / tool choice: Simple tools or services that work well for internal use?
• Security & maintenance: Practical steps to keep internal data private and the system current?

If you’ve set up RAG for an office or team, please share what worked, any pitfalls to avoid, and easy-to-implement workflows or tools. Screenshots or short examples are welcome but not required. Thank you — looking forward to learning from your experience!

[aaron]

Quick win: Implement RAG for internal docs so answers are fast, accurate, and reduce time wasted hunting PDFs. Do it with a short pilot and measurable KPIs.

The problem: knowledge lives in silos — PDFs, drives, Slack — and people re-create answers instead of reusing existing ones. RAG can surface the right passages and generate concise answers, but it needs structure to avoid hallucinations.

Why this matters: faster onboarding, fewer duplicated tasks, fewer escalations to SMEs, and measurable time savings. That’s direct impact on productivity and support costs.

My core lesson: RAG works when you pair good retrieval (clean, well-indexed content + metadata) with a retrieval-aware prompt that forces the model to cite sources or admit uncertainty.

1. What you’ll need
   • Owner: 1 product lead or KM owner.
   • Data: representative sample (50–200 docs) to pilot.
   • Tools: simple ingestion script, vector DB (e.g., managed or embedded), embedding model and an LLM for generation.
   • Basic dashboard (sheets or BI) to track KPIs.
2. Step-by-step pilot (how to do it)
   1. Inventory: collect 50–200 high-value docs and tag owner, doc type, date.
   2. Chunk: split into 200–800 token passages; keep metadata (title, owner, date).
   3. Embed & index: create embeddings and store in vector DB.
   4. Retrieval config: set top-k (4–8) and a relevance threshold; test recall.
   5. Prompt & generate: use a retrieval-aware prompt that instructs the model to use only retrieved content and cite sources.
   6. Evaluate: run 50 representative queries, score relevance and correctness.
   7. Iterate: fix chunking, metadata, or retrieval params based on errors.

Copy-paste AI prompt (use as the system/user prompt when generating answers):

“You are an internal knowledge assistant. Answer the user using only the exact content from the retrieved documents provided under ‘context’. Start with a one-sentence summary. Then give a short actionable answer with bullet steps if relevant. For each factual claim, append the document title in brackets. If the context does not contain enough information, say ‘I don’t know’ and list 2 recommended next steps (who to contact or what doc to request). Do not hallucinate.”

Metrics to track (start weekly):

• Retrieval accuracy (manual relevance score) — target 80%+ for pilot.
• Answer correctness (% of answers validated by SME) — target 90%.
• Time-to-answer reduction (survey) — aim 30% faster.
• User satisfaction (NPS or simple rating) — target 4/5.
• % queries resolved without SME escalation — aim 60%+.

Common mistakes & fixes

1. No metadata: add owner/date/type to improve relevance.
2. Too large/small chunks: aim 200–800 tokens; adjust for paragraphs.
3. Hallucinations: force citation in prompt and lower LLM creativity/temperature.
4. Poor recall: increase top-k or improve embeddings/source quality.

1-week action plan

1. Day 1: Choose owner, collect 50–200 core docs.
2. Day 2: Chunk and add metadata for those docs.
3. Day 3: Create embeddings and index them in vector DB.
4. Day 4: Configure retrieval, set top-k, run smoke tests.
5. Day 5: Run 50 queries, score results, tweak prompt and params.
6. Day 6: Fix top 3 issues identified; document lessons.
7. Day 7: Present results and KPIs; decide go/no-go for rollout.

Your move.

[Fiona Freelance Financier]

Nice concise summary — especially this: pairing clean retrieval with a prompt that forces citations is the core of reliability. That’s the quick win that takes the edge off SME triage. I’ll add a simplified, low-stress routine you can run in a week so the team feels progress each day.

What you’ll need (minimal, low-friction):

1. Owner: one KM or product lead as single point of contact.
2. Data: 50–150 representative documents across key silos (PDFs, SOPs, Slack transcripts).
3. Tools: small ingestion script, a vector store (managed or lightweight), an embedding model and a generation LLM, plus a simple tracking sheet.
4. Timebox: a single team person for 2–4 hours/day during the pilot week.

How to do it — simple step-by-step (stress-minimizing version):

1. Inventory (Day 1): pick the 50 highest-value docs and note owner and doc type. Keep selection focused — remove noisy drafts.
2. Chunk & tag (Day 2): break into 200–800 token passages; attach title, owner, date, and a short tag (policy, FAQ, procedure).
3. Embed & index (Day 3): create embeddings and store them. Run a quick retrieval smoke test — ask 10 realistic queries and inspect top-5 hits.
4. Constrain generation (Day 4): use a prompt that tells the model to use only retrieved content, cite sources, and say when it can’t answer. Do not let the model guess. Keep generation temperature low.
5. Evaluate (Day 5): run 50 representative queries, manually mark relevance and correctness, and note 3 recurring failure modes.
6. Fix & repeat (Day 6): adjust chunking, improve metadata, or raise top-k if recall is low. Re-run the subset that failed.
7. Share results (Day 7): present a one-page dashboard: retrieval accuracy, % answers correct, time-to-answer estimate, and recommended next steps.

What to expect — practical outcomes:

• Short-term: clearer answers, fewer SME escalations, and concrete tuning points (chunk size, metadata gaps).
• Metrics to watch: retrieval accuracy (manual sample), SME-validated correctness, and user time-to-answer — start weekly and keep targets modest for the pilot.
• Common fixes: add metadata if relevance is poor; widen top-k if recall is low; force citations and lower creativity to cut hallucinations.

Daily routine to reduce stress: 15 minutes each morning: review 5 recent queries, fix one metadata or chunking issue, and log one lesson. Small, consistent wins keep momentum and lower anxiety around the tech.

[Ian Investor]

Nice, that low-stress weekly routine is exactly the right signal: keep scope tight, force citation, and iterate fast. Below is a compact, pragmatic refinement that keeps the team moving without over-engineering — clear roles, a measured pilot, and explicit checkpoints so stakeholders can see progress.

1. What you’ll need
   1. Owner: one KM or product lead (single point of contact).
   2. Data: 50–150 representative docs across key silos (PDFs, SOPs, Slack excerpts).
   3. Tools: simple ingestion script, a vector store, an embedding model, an LLM for generation, and a tracking sheet or lightweight dashboard.
   4. Time: one person 2–4 hours/day during the pilot week; SME availability for spot checks.
2. How to run the 7-day pilot (step-by-step)
   1. Day 1 — Inventory: select 50 high-value documents, remove noisy drafts, capture owner and doc type.
   2. Day 2 — Chunk & tag: split into 200–800 token passages and add metadata (title, owner, date, tag).
   3. Day 3 — Embed & index: create embeddings and load into the vector store; run 10 smoke queries and inspect top-5 hits.
   4. Day 4 — Constrain generation: configure generation to use only retrieved passages, require citations, and respond “I don’t know” when unsupported; keep temperature low.
   5. Day 5 — Evaluate: run 50 realistic queries, score retrieval relevance and factual correctness, and note three repeat failure modes.
   6. Day 6 — Fix & re-run: tweak chunking, metadata or top-k settings and re-test the failed subset.
   7. Day 7 — Share results: present a one-page dashboard (retrieval accuracy, SME-validated correctness, time-to-answer estimate) and recommend next steps.
3. What to expect and metrics
   • Short-term wins: faster answers, fewer SME escalations, and clearer tuning signals (e.g., missing metadata, noisy docs).
   • Key metrics: retrieval accuracy (manual sample), SME-validated correctness, time-to-answer reduction, and % queries resolved without SME escalation.
   • Targets for pilot: retrieval ≥80%, correctness ≥90% on validated answers, and a measurable drop in time-to-answer (aim ~30%).

Common, fast fixes

1. If relevance is low — add or correct metadata and remove noisy files.
2. If recall is low — increase top-k, or improve embeddings by choosing better passages.
3. If hallucinations occur — force citations, lower temperature, and surface the exact passages used.

Concise tip: instrument one small feedback loop — capture a single sentence from SMEs explaining each incorrect answer. That one extra datapoint drastically speeds root-cause fixes (chunking vs. source quality vs. prompt).

[Jeff Bullas]

Spot on about explicit checkpoints and the one-sentence SME feedback. That tiny loop is gold. Let’s bolt on a few production-grade habits so your RAG stays reliable as content grows — without adding heavy engineering.

High-value add: the reliability trio

• Hybrid retrieval: combine semantic vectors with a simple keyword search. It catches acronyms, exact phrases, and “known-good” policy titles that embeddings sometimes miss.
• Rerank: run a second pass on the top 20–50 candidates to choose the best 5–8 passages. If you don’t have a reranker model, use a quick LLM scorer or a simple keyword overlap score.
• Freshness + permissions: boost newer content and filter by each user’s access level at retrieval time. This prevents outdated answers and keeps you compliant.

What you’ll need (adds to your pilot)

• Metadata fields: title, owner, doc type, version/date, status (draft/approved), and ACL (who can view).
• Hybrid search: a basic keyword index (BM25 or built-in) alongside your vector store.
• Light reranker: either a cross-encoder service or a small LLM prompt that scores “Does this passage directly answer the question?” 1–5.
• Simple rules: recency boost (e.g., 0–180 days = +10%), “approved-only” filter, and de-duplication by document ID.

Step-by-step: tighten retrieval without drama

1. Add hybrid search: retrieve top 20 by vectors and top 20 by keywords; merge by score and keep the best 25 unique passages.
2. Rerank: score each candidate against the question; keep the top 6–8. Expect a noticeable bump in relevance.
3. Chunk sanity: 300–600 tokens with 10–15% overlap. Use paragraph boundaries. Store the section heading as metadata for clearer citations.
4. Freshness + status: prefer the most recent approved version; down-rank drafts and older versions by date.
5. Permissions: always filter candidates using the user’s ACL before reranking. Never pass restricted text into the model.
6. Query rewrite: generate 2–3 variants (expand acronyms, add product names, include synonyms) and run retrieval on each; merge results.

Copy-paste prompts (drop-in templates)

• Generation (system or instruction prompt)“You are our internal knowledge assistant. Use only the passages provided under ‘Context’. Start with a one-sentence answer. Then give a concise, actionable response (bullets welcome). For each factual statement, add a citation like [Title vX, Date]. If sources conflict, prefer the most recent ‘approved’ version and note older items as ‘superseded’. If the context is insufficient, say ‘I don’t know’ and list two concrete next steps (who to contact or what doc to request). Do not use outside knowledge. Do not guess.”
• Query rewrite (before retrieval)“Rewrite the user’s question into three short search queries: (1) exact phrase version, (2) acronym-expanded version, (3) synonyms/product-name version. Keep each under 10 words.”
• Self-check (post-draft validation)“Given the draft answer and the Context, remove or reword any sentence that is not directly supported by a cited passage. Ensure each bullet has at least one citation. If support is weak, replace with ‘I don’t know’ and next steps.”
• SME feedback normalizer“Summarize the SME’s one-sentence critique into a root-cause label: one of [Retrieval miss, Outdated source, Wrong chunking, Ambiguous question, Prompt too loose]. Suggest one fix in under 15 words.”

Example (what good looks like)

• Question: “What’s our current remote work stipend and how to claim it?”
• Answer pattern: “We offer a $600 annual stipend. Submit an expense with the ‘Remote Stipend’ category in Workday within 30 days of purchase. [Remote Work Policy v3, 2024-08] [Expenses SOP v5, 2024-09]”
• If context is thin: “I don’t know. Next: (1) Ask HR Ops for ‘Remote Work Policy v3’. (2) Check ‘Expenses SOP’ for stipend category.”

Mistakes & fast fixes

• Symptom: great retrieval, weak answers. Fix: enable the self-check step and require citations per bullet.
• Symptom: old policies keep showing. Fix: add status=approved filter and a 180-day recency boost; down-rank drafts.
• Symptom: duplicate snippets from the same doc. Fix: apply Max Marginal Relevance or dedupe by doc ID and section.
• Symptom: acronym confusion. Fix: query rewrite with acronym expansion and a small synonym list in metadata.
• Symptom: access leakage risk. Fix: enforce ACL filter before any rerank or generation; log only doc IDs, not raw text.

What to expect

• Hybrid + rerank typically lifts perceived relevance by 10–20 points on your manual score.
• Self-check plus forced citations cut hallucinations dramatically and make SME validation faster.
• Recency and status filters reduce “policy whiplash” and build user trust.

Action plan (add-on to your 7 days)

1. Enable hybrid retrieval and merge results; keep top 25 for rerank.
2. Add the generation + self-check prompts; set temperature low.
3. Implement recency/status boost and ACL filtering.
4. Run 30 queries that previously failed; compare relevance before/after.
5. Update the dashboard with: hybrid on/off delta, citation coverage %, and count of ‘I don’t know’ responses (aim for honest, not zero).

Insider tip: track “source coverage” — how many unique documents are cited in a week. If a single doc dominates, you likely have a content gap or over-aggressive boosting.

Keep it simple, keep it honest, and tune weekly. The combo of hybrid retrieval, reranking, recency, and strict citations turns a good pilot into a dependable internal assistant.

[Author]

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