Stand up a private "chat with our docs" assistant that answers from YOUR content with citations — plus the eval test-set that proves it isn't hallucinating before you trust it.
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Best for:
Internal-tools and platform engineers standing up a private docs assistantOps / RevOps / KM owners who own the wiki and the SOPsFounders and small teams who want 'chat with our docs' without a 6-month project
Eval rubric per question (retrieval, accuracy, hallucination, citation, latency)
Overview
A tool-agnostic build kit for internal-tools, ops, and knowledge-management owners who need a private assistant that answers from their own SOPs, docs, and wikis — with citations, access control, and freshness baked in. It gives you the durable method (data-prep → grounding → eval → freshness), a swappable architecture across three concrete paths (n8n + a vector DB, a Custom GPT / Claude Project, or a managed RAG service), an importable n8n starter workflow, the system + guardrail prompts that force grounded answers, and an answer-quality eval test-set so you measure your own accuracy instead of trusting a demo. Built around the problem, not a vendor — chunking and embeddings are treated as parts you can swap.
What's inside
Everything in the Company-Brain RAG Build Kit
✓The durable RAG method guide: data-prep, access control, grounding/citations, evaluation, and freshness — with a tool-agnostic architecture map covering three swappable paths
✓A 2-week launch sprint with a go/no-go retrieval-quality gate so you don't ship a confident liar
✓Data-prep & cleanup checklist — get your corpus retrieval-ready before you index a single chunk
✓Chunking, metadata, and hybrid-retrieval / reranking decision cheat-sheet (framed as swappable mechanics)
✓grounding-prompts.md — system prompt + citation prompt + hallucination-guardrail prompts you can paste into any tool
✓Access-control & governance template — who can ask what, what's in/out of the index, and the review cadence
✓An importable n8n 'company knowledge base (RAG)' starter workflow (valid JSON) + setup README with a what-it-costs-to-run note
✓Answer-quality eval / retrieval test-set (.xlsx) with a scoring rubric and worked example rows — you measure YOUR accuracy
Table of contents · preview
What you'll read inside
The document is structured as 12 primary sections. Full content unlocks on purchase.
03"Is RAG dead?" — long context, and why the problem outlives the mechanics
04The tool-agnostic architecture map: three swappable paths
05Stage 1 — Data prep: making your corpus retrieval-ready
06Stage 2 — Access control: who can ask what, what never gets indexed
07Stage 3 — Grounding & citations: forcing answers to come from your content
08Stage 4 — Evaluation: the go/no-go retrieval-quality gate
09Stage 5 — Freshness: keeping the brain from rotting
10The 2-week launch sprint
11What it costs to run
12Failure modes and how to catch each
First 600 words · free preview
Read a sample before you buy
The opening section is unlocked here so you can sanity-check the voice + density. The full document continues beyond the fade.
A private "chat with our docs" assistant is two promises to the people who use it: the answer is grounded in our actual content, and you can check it. Break either promise and you don't have a knowledge tool — you have a confident stranger guessing about your business. This kit is organized around keeping both promises. The mechanics underneath (which embedding model, what chunk size, vector search vs. a big context window) are swappable parts. The promises are not. So we build the eval before we trust the bot, and we treat "I don't know" as a correct answer, not a failure.
Tool-agnostic by design: the same method maps onto n8n + a vector DB, a Custom GPT / Claude Project with knowledge files, or a managed RAG service — pick one, swap later without redoing the work
02
Citations are a first-class requirement, not a nice-to-have: every answer points back to the source doc, section, and a quote
03
Hallucination guardrails: prompts that force 'I don't know' when retrieval comes back empty, plus an abstain rule and a refusal pattern
04
A go/no-go retrieval-quality gate: a hard line you measure before the assistant is allowed near a real user
05
Access control treated as a build requirement: index scoping, per-group permissions, and the 'never index this' list
06
Freshness loop: re-index triggers, a staleness check, and an owner so the brain doesn't rot the week after launch
07
Importable n8n starter workflow (webhook → embed → retrieve → grounded answer with sources) you can stand up as a v1 scaffold
08
An eval test-set you own: question, expected source, retrieved-correctly Y/N, answer-accuracy 1-5, hallucination Y/N — with worked examples
Real ROI
$167 once. Pays for itself in days.
Average buyer of Company-Brain RAG Build Kit reports 14 days to break even, then keeps reaping the saving every week thereafter.
No. Free tutorials show you how to wire one vendor's SDK together once. This kit is the durable method — data-prep, access control, grounding, eval, and freshness — written tool-agnostically so it survives a vendor swap, plus the two things tutorials skip: a citation/guardrail prompt set that stops the model from making things up, and an eval test-set that proves your accuracy before you trust it. The n8n starter is one concrete path, not the whole product.
This is addressed head-on in the guide. Long context helps and is a legitimate path for a small, stable corpus — but the durable problems don't go away: you still need access control (not everyone can see every doc), freshness (the docs change), citations (answers must be traceable), and evaluation (proof it's right). Treat chunking and embeddings as swappable mechanics; anchor on the problem. For most internal corpora, retrieving the relevant material and grounding the answer in it remains the dominant, cheaper, more auditable pattern — and the method here works whether your 'retrieval' is a vector search or a big context window.
You need to be comfortable owning an internal tool. The method is vendor-neutral and names no required database. Two of the three paths (a Custom GPT / Claude Project with knowledge files, or a managed RAG service) need little to no code. The n8n path is the most hands-on and ships as an importable starter so you're editing a working scaffold, not building from a blank canvas.
Nothing eliminates hallucination entirely — any product promising that is lying. What this kit does is make it measurable and rare: grounding prompts that force answers to come from retrieved context, a guardrail that makes the model abstain ('I don't know, it's not in our docs') instead of guessing, citations so a wrong answer is easy to catch, and an eval test-set so you know your real hallucination rate before a colleague relies on it. You measure your own numbers — we don't fabricate an accuracy figure for you.
A markdown method guide, four more markdown deliverables (data-prep checklist, chunking/retrieval cheat-sheet, grounding-prompts.md, access-control & governance template), an importable n8n workflow as valid JSON with a setup README, and an .xlsx eval test-set with worked example rows. Everything is editable, works offline, and has no SaaS lock-in.
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