The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable.
A knowledge base earns its name only if people can find what they need and the answers stay current — most die as a folder of stale documents nobody trusts. This project builds one that works two ways at once: searchable by humans and retrievable by AI. It plans the taxonomy around the questions people actually ask, structures the content so the right article is one search away, writes the articles clearly, tags the metadata that makes entries filterable, prepares everything so an AI assistant can ground its answers in it, and ships it in a form the team can maintain. It owns the knowledge-repository outcome specifically — it is not a public documentation website (that's product docs for outside readers) and not a content engine (that produces marketing pieces) — so every decision serves a trustworthy, findable, AI-friendly internal knowledge store. Each stage connects to a NewPrompt workflow you can run on its own; together they turn scattered institutional knowledge into a system. You own the knowledge; the project makes sure it's organized, retrievable, and maintainable instead of a graveyard of docs.
Each stage runs a NewPrompt workflow, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
See the execution map →Plan the knowledge taxonomy
Decide what knowledge the base must hold and how it's organized — around the questions people actually ask, not the org chart — so the structure reflects how knowledge is sought, not how it was filed.
Structure it for search
Organize the taxonomy into an information architecture — categories, hierarchy, cross-links — so a person lands on the right article in one or two steps instead of scrolling a flat list of titles.
Write the knowledge articles
Write the entries themselves — clear, consistent, self-contained — so each article answers its question on its own and reads the same whether a person or an AI assistant surfaces it.
Tag the metadata
Extract and attach the metadata each entry carries — topic, owner, last-reviewed, audience — so the base can be filtered, kept current, and surfaced precisely instead of searched by keyword alone.
Make it AI-retrievable
Prepare the knowledge base for retrieval — chunked and grounded — so an AI assistant can answer from it accurately and admit when the base doesn't cover a question, turning a static repo into a source an AI can use.
Ship it maintainable
Deploy the knowledge base with a publishing and review path you can rerun — so updating an article is routine, not a project, and the base stays current instead of decaying into the thing nobody trusts.
A knowledge base that's actually used — a taxonomy built around real questions, content structured for search, clear articles, metadata that makes entries filterable, everything prepared for AI retrieval, and the whole thing shipped maintainable — knowledge people and AI can both find and trust, not a folder of stale docs.
A knowledge base is structured to be retrieved — by search and by AI. The journey emphasizes taxonomy, metadata, and AI-retrievability over page design.
Because a knowledge base often feeds an assistant or RAG system. Tagging metadata and structuring for search up front is what makes that retrieval accurate later.
Yes — writing the articles is a stage, but it comes after the taxonomy and structure so the content lands in the right place.
Start with the taxonomy, not the articles. This blueprint runs six stages: plan a taxonomy around the questions people actually ask, structure it for search, write self-contained articles, tag metadata, prepare it for AI retrieval, then ship it with a rerunnable maintenance path. You own the knowledge itself; the blueprint structures how it's captured and found.
Gather your scattered institutional knowledge and the real questions people ask before stage one. The taxonomy stage organizes around those questions, not your existing folders, so bring support tickets, internal wikis, tribal know-how, and per-entry metadata like topic, owner, and last-reviewed date. You supply the source material; the workflows help structure and write it.
Yes, that reuse is the point. Stage five prepares the base for AI retrieval — chunked and grounded — precisely so an assistant or RAG system can answer from it and admit gaps. The metadata you tag in stage four makes retrieval precise later. You wire it into your own assistant; the blueprint gets the knowledge retrieval-ready.
Decide what to publish and why before you write a word — set the business goals and audience, map needs to topics, brief the priority pieces, then turn it into a content plan you publish against.
Organize a site so people and crawlers find things — inventory the content, group it into a real hierarchy, design the sitemap and navigation, then document the information architecture for the build.
Turn source material and a target audience into clear docs, help, or knowledge-base content — gather the material, outline the piece, write it section by section, then check it for clarity and gaps.
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Prepare documents for a RAG system so retrieved answers stay accurate — budget the chunk size to the model, ground the sources against drift, and split them on clean boundaries for retrieval.
Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.
A sequential prompt workflow for content strategy: audience first, then goals, competitor gaps, topic clusters, defensible angles, and a publishing plan with a review loop.
A reference document is only as useful as its addressability. Heavy § labeling turns a nine-section reference into something every answer can point into.
Overview, Key Concepts, How It Works, Usage, Troubleshooting — docs pages with the same shape for every feature, and code in every usage section.
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
RAG context is a budget with line items: retrieved documents, the question, and the answer all share one window. Plan how many chunks actually fit.
SHIP or DO NOT SHIP: twelve checks on failure modes, observability, rollback, and load — the review that happens before the incident.
Turn website goals, audiences, content types, and conversion paths into a sitemap and information-architecture spec: navigation, a page inventory, hierarchy, URLs, user journeys, and an internal-linking plan.
Break a big goal into a sequential prompt workflow — each step with an objective, prompt, and expected output.
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
Build code explanation prompts — mode, audience, and depth turn into an understanding contract that teaches, not narrates.
Build prompts that extract defined fields from unstructured text — emails, invoices, tickets, résumés.
Will this fit the model's context window? Token budget planning, range-honest fit verdicts, and model comparison.
Practical project ideas you can build from this base project path — each opens in the Project Advisor.
The full path to docs people can actually navigate — plan the doc set, structure the site, write the guides and the API reference, then ship it as a coherent documentation site.
The full path to a retrieval system that returns grounded answers — understand the corpus, chunk and ground it, extract and classify the metadata, then evaluate that retrieval actually works.
The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy.