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Use the 30 templates below as a starter schema for capturing domain knowledge before you build elaborate folders, page types, or retrieval rules. They cover personal reference, learning, work, business, technical practice, and creative work. Each template tells you what to record, shows a compact example, and suggests a question an LLM could answer from a completed page.
This is an editorial selection, not a canonical industry list. The useful constant is the schema: every note keeps claims, evidence, context, open questions, and next actions together so a human or an LLM can inspect and reuse it.
What an LLM knowledge base actually does
An LLM knowledge base makes your own material available at answer time without retraining the underlying model. In a common retrieval-augmented generation (RAG) design, documents are converted to text, split into manageable chunks, converted into embeddings, and stored in a vector index that preserves each chunk’s relationship to its original document. A question is embedded, similar chunks are retrieved, and those chunks are supplied as context to the model.
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Amazon Web Services describes this relationship directly: “The pieces or chunks are then converted to embeddings and written to a vector index, while maintaining a mapping to the original document.” That source mapping is what lets you inspect where an answer came from and discover stale or conflicting material.
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A different pattern is a compiled Markdown wiki. You place raw notes and source files in an inbox, then an LLM follows an instruction and schema file to create or update structured pages and indexes. One documented project also adds a learning layer, flashcards, and gap tracking. Those are features of that project’s design, not requirements for every knowledge base.
The templates in this article work in either pattern. In a wiki, they become Markdown page types. In RAG, they provide consistent source documents whose chunks carry predictable labels and evidence.
Start capturing in five controlled steps
- Create an inbox. Make an
inbox/orraw/directory for transcripts, PDFs, bookmarks, meeting notes, and quick observations. Do not force every item into a final category on arrival. - Write one instruction file. Define allowed page types, naming rules, required evidence fields, how to mark uncertainty, and what the LLM must do when sources conflict. Keep this file under version control with the templates.
- Use a stable record shape. At minimum, capture title, summary, entities, claims, source links or file paths, decisions, open questions, and next actions. Add domain-specific fields from the templates below.
- Process a small batch. Feed the LLM a few representative files, inspect every generated page, and adjust the schema before importing a large archive. This catches missing fields and overconfident summaries early.
- Keep provenance and review dates. Store the original path or citation next to each extracted claim. Add a last-reviewed date and a status such as confirmed, needs review, or superseded; revisit unresolved and aging pages incrementally.
Templates improve consistency; they do not guarantee accurate answers. Source quality, chunking, retrieval settings, model behavior, and evaluation still determine whether a response is trustworthy. Treat evaluation as a separate stage rather than assuming a well-formatted page is correct.
The 30 domain templates
Each template below is intentionally small enough for a single Markdown file or database record. Keep the common fields in every page, then add the specialized fields shown for the domain.
Personal reference
1. Interests and topics
Purpose: Track subjects you follow over time. Fields: topic, why it matters, key terms, trusted sources, current view, related topics, review date. Example: “Urban gardening — focus on balcony containers; sources: local extension notes; current view: herbs tolerate partial shade.” LLM question: “Which of my interests overlap with water-saving projects?”
2. Reading notes
Purpose: Preserve what a book, article, or paper actually contributed. Fields: citation, central claim, supporting passages, your response, applications, unresolved points, tags. Example: “Designing Data-Intensive Applications — claim: reliability is a system property; application: add failure scenarios to architecture reviews.” LLM question: “What reading notes support a staged rollout?”
3. People and relationships
Purpose: Remember context for professional or personal contacts without storing unnecessary sensitive data. Fields: name, relationship, interests, commitments, last interaction, preferred channel, follow-up date, consent or privacy note. Example: “Sam — former teammate; interested in observability; promised to send a conference link by June 12.” LLM question: “Who has asked for the observability link?”
4. Places
Purpose: Collect practical knowledge about locations. Fields: place, purpose, address or map reference, hours, accessibility, cost notes, personal rating, source date. Example: “Riverside trail — morning walk; paved first 2 km; parking information checked April 2026.” LLM question: “Which saved places suit a short accessible walk?”
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5. Personal projects
Purpose: Turn an idea into a trackable project. Fields: outcome, scope, constraints, milestones, decisions, dependencies, next action, status. Example: “Home sensor dashboard — display temperature and humidity; constraint: local network only; next action: test the data schema.” LLM question: “What is the next unblocked action on my projects?”
Learning and research
6. Concept explainer
Purpose: Build a plain-language explanation of one idea. Fields: definition, prerequisites, mechanism, analogy, examples, misconceptions, sources, confidence. Example: “Eventual consistency — replicas converge after updates propagate; misconception: it does not mean writes are lost.” LLM question: “Explain eventual consistency using only my notes.”
7. Paper or study
Purpose: Separate a study’s evidence from your interpretation. Fields: citation, research question, method, population or dataset, findings, limitations, quoted figures, relevance. Example: “Study X — observational sample of 240 teams; association reported, no causal conclusion; limitation: self-reported data.” LLM question: “What limitations should I mention before applying this finding?”
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Purpose: Organize material from a course into retrievable units. Fields: lesson, objectives, prerequisites, notes, exercises, mistakes, references, mastery status. Example: “SQL joins — objective: choose join type from relationship; mistake: filtering the outer table in the wrong clause.” LLM question: “Create a practice sequence for lessons I marked weak.”
9. Research question
Purpose: Prevent open investigations from becoming vague collections of links. Fields: precise question, scope, known facts, hypotheses, evidence needed, search log, contradictions, decision deadline. Example: “Can local indexing meet our latency target? Known: 2 million records; missing: benchmark under concurrent queries.” LLM question: “Which evidence would most reduce uncertainty?”
10. Experiment
Purpose: Make trials reproducible. Fields: hypothesis, variables, setup, procedure, measurements, result, deviations, conclusion, next experiment. Example: “Chunk-size trial — hypothesis: shorter chunks improve citation precision; compare two fixed sizes on the same question set.” LLM question: “Which experimental result contradicts the current assumption?”
Work and operations
11. Role knowledge
Purpose: Capture durable knowledge for a job or responsibility. Fields: responsibility, stakeholders, systems, recurring deadlines, standards, escalation path, risks, glossary. Example: “Release coordinator — owns go/no-go checklist; escalation: service owner then incident lead.” LLM question: “What must happen before a release decision?”
12. Process or SOP
Purpose: Describe a repeatable operation. Fields: trigger, prerequisites, numbered steps, decision branches, owner, expected output, rollback, review date. Example: “Access review — export account list, manager confirms, revoke exceptions, archive approval record.” LLM question: “What is the rollback if the export is incomplete?”
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13. Meeting record
Purpose: Make meetings useful after they end. Fields: date, attendees, purpose, decisions, rationale, action owner, due date, unresolved issues, source transcript. Example: “April 8 planning — decided to defer migration; Alex owns dependency inventory by April 15.” LLM question: “Which decisions still lack an owner?”
14. Decision record
Purpose: Preserve why a choice was made. Fields: decision, context, options, criteria, selected option, rejected alternatives, consequences, revisit trigger. Example: “Use managed object storage — selected for lifecycle controls; revisit if regional residency changes.” LLM question: “What condition would reopen this decision?”
15. Recurring task
Purpose: Turn periodic work into a reliable checklist. Fields: cadence, trigger, checklist, inputs, expected output, time estimate, failure handling, last completed date. Example: “Monthly access audit — run on the first business day; output: signed exception report.” LLM question: “What is due next week and what inputs are missing?”
Business and product
16. Customer or stakeholder
Purpose: Keep needs and commitments distinct from assumptions. Fields: segment, goals, constraints, evidence, requests, commitments, objections, consent and retention notes. Example: “Small clinic — needs offline export; request heard in two interviews; no commitment made.” LLM question: “Which requests recur across stakeholder records?”
17. Market segment
Purpose: Define a segment with evidence rather than labels. Fields: inclusion rule, jobs to be done, pains, alternatives, buying constraints, evidence dates, uncertainty. Example: “Two-person agencies — need client approvals without a dedicated administrator; evidence from three discovery calls.” LLM question: “What evidence separates this segment from freelancers?”
18. Competitor or alternative
Purpose: Record comparable facts and avoid stale claims. Fields: offering, target user, capabilities, limitations, pricing source and date, differentiators, confidence. Example: “Alternative A — supports export; pricing page checked May 2026; integration depth not verified.” LLM question: “Which alternatives meet the offline requirement?”
19. Product requirement
Purpose: Connect a requested capability to a testable outcome. Fields: user, problem, requirement, acceptance criteria, priority, dependencies, non-goals, evidence. Example: “Export filtered records — CSV must preserve selected columns and timezone; non-goal: scheduled exports.” LLM question: “Which requirements lack acceptance criteria?”
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Purpose: Coordinate a release across teams. Fields: objective, audience, milestones, owners, dependencies, readiness checks, communication, rollback, risks. Example: “Workspace launch — pilot 20 users, publish help page, monitor error rate, keep previous workflow available.” LLM question: “What could block the pilot date?”
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Technical practice
21. Codebase
Purpose: Give an LLM architectural context before it suggests changes. Fields: repository, purpose, runtime, entry points, modules, build and test commands, deployment path, owners, known debt. Example: “Billing service — Python worker; tests run with the project’s documented command; payments module owns retries.” LLM question: “Where should validation for a new billing field live?”
22. API or integration
Purpose: Record how a system is safely called. Fields: endpoint or event, authentication, request and response shape, limits, errors, retries, version, official source, example payload. Example: “Inventory webhook — signed POST; duplicate events possible; handler must be idempotent.” LLM question: “How should the client handle a duplicate event?”
23. Architecture decision
Purpose: Make system structure understandable months later. Fields: context, constraints, options, decision, data flow, operational impact, rejected options, review trigger. Example: “Queue between services — absorbs bursts; adds eventual consistency; revisit at sustained throughput threshold.” LLM question: “What trade-off did the queue introduce?”
24. Incident record
Purpose: Learn from failures without reducing them to blame. Fields: timeline, impact, detection, contributing factors, mitigation, root causes or hypotheses, follow-ups, evidence, severity. Example: “May 3 timeout — detection from latency alert; mitigation was traffic reduction; follow-up adds dependency timeout.” LLM question: “Which follow-ups address detection rather than mitigation?”
25. Runbook
Purpose: Help an operator act under pressure. Fields: symptom, prerequisites, commands or UI path, expected output, branching conditions, escalation, rollback, last verification. Example: “Queue backlog — check consumer health, compare lag to threshold, scale only after confirming downstream capacity.” LLM question: “What should an operator verify before scaling?”
Creative practice
26. Idea bank
Purpose: Store raw concepts without losing their origin. Fields: idea, audience, hook, source spark, constraints, related ideas, status, next test. Example: “Short guide to household sensors — audience: renters; constraint: no drilling; next test: interview two readers.” LLM question: “Which ideas fit a no-hardware installation constraint?”
27. Creative brief
Purpose: Align a project before drafting. Fields: objective, audience, message, tone, deliverable, mandatory elements, exclusions, references, approval criteria. Example: “Explainer video — message: backups need recovery tests; tone: practical; approval requires a working demonstration.” LLM question: “What must the draft avoid?”
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28. Style reference
Purpose: Describe a style in observable terms rather than vague praise. Fields: reference, palette or vocabulary, structure, pacing, do, do not, licensing or permission note. Example: “Reference article — short paragraphs, concrete verbs, diagrams before code; do not imitate distinctive phrases.” LLM question: “Which style rules apply to this draft?”
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29. Draft and revision
Purpose: Keep versions, feedback, and unresolved choices together. Fields: draft status, audience, version, change log, feedback with source, accepted changes, rejected changes, next pass. Example: “Guide v3 — accepted request for troubleshooting table; rejected jargon-heavy opening; next pass checks headings.” LLM question: “Which feedback remains unresolved?”
30. Publishing record
Purpose: Make publication and later maintenance auditable. Fields: title, channel, publish date, owner, assets, rights, metadata, update schedule, corrections, canonical source. Example: “Knowledge-base guide — published June 2026; review every six months; source files stored in the project archive.” LLM question: “Which published items are due for review?”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Markdown wiki or managed RAG?
Both approaches can expose the same captured knowledge, but they put control and maintenance in different places.
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|---|---|---|
| Setup | Copy templates, place source files in an inbox, and run the documented compilation workflow. | Configure data sources, embedding choices, indexing, retrieval, and access controls. |
| Control | Human-readable files, explicit filenames, schemas, and instructions are easy to inspect and change. | Less pipeline code to maintain, but provider models, limits, and settings shape behavior. |
| Retrieval | Navigate indexes and pages, then ask an LLM to reason across selected files. | Retrieve semantically similar chunks and add them to the model context automatically. |
| Customization | Change templates, instructions, naming, and review workflow directly. | Use supported file sources and provider options such as retrieval filters or reranking where available. |
| Best fit | Manageable text collections, inspectable structure, local ownership, or strict change control. | Hosted ingestion and retrieval for teams willing to configure and operate a provider account. |
| Main maintenance | Keep links, indexes, schemas, and generated pages synchronized. | Monitor ingestion, embedding configuration, permissions, costs, retrieval quality, and provider changes. |
DigitalOcean’s knowledge-base documentation notes that the embedding model cannot be changed after a knowledge base is created and describes optional reranking. Confirm such constraints before importing a large corpus. Provider features, supported inputs, prices, and limits change, so consult current official documentation when you configure an account.
AWS also publishes flow templates, including a knowledge-base flow example. Those flows require the supporting AWS resources to exist first, and users are responsible for cleaning up resources they no longer need. A managed service reduces pipeline work; it does not remove configuration, privacy, or lifecycle decisions.
Design rules that keep answers trustworthy
- Separate facts from interpretation. Label direct observations, quoted claims, calculations, and your own conclusions.
- Attach evidence at claim level. A page-level bibliography is weaker than a source path or citation beside the statement it supports.
- Record conflicts instead of blending them. Preserve both versions, identify dates and authorities, and mark the conflict for review.
- Make freshness visible. Store publication and last-reviewed dates, especially for APIs, prices, policies, and operational procedures.
- Control sensitive data. Exclude secrets, unnecessary personal details, and material your model or provider is not permitted to process. Apply access rules before indexing.
- Evaluate with real questions. Build a small test set covering ordinary, ambiguous, and unanswerable questions. Check retrieval, citations, refusal behavior, and stale-page handling separately.
- Keep a deletion path. Know how to remove a source from raw storage, generated pages, indexes, caches, and backups when retention rules require it.
What this 30-template set gives you
You can begin with a plain folder of Markdown files and move to vector retrieval later without discarding the capture work. The common fields preserve provenance and uncertainty; the domain fields make pages useful for a specific activity. Start with the five templates that match your immediate work, process a small batch, and expand only when a recurring question cannot be answered cleanly.
No supported statistic establishes that any particular template set improves capture speed, answer accuracy, or productivity. The practical test is whether your own evaluation questions become easier to answer with traceable evidence after you adopt the schema.
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