Free Implementation Guide

    Graph Engineering,
    With Receipts

    Build a small, evidence-backed relationship layer for one real work question. Learn when to use a graph, how to keep it honest, and how to prove that it improved an answer.

    The useful version

    A graph is a decision system, not a storage trend.

    The guide starts with a question, a source register, and a measurable answer. Only then does it add entities and relationships. Every important edge has evidence, freshness, confidence, and a review state.

    01 / Recall the right entities and relationships
    02 / Capture corrections and missing context
    03 / Review stale, conflicting, and unsupported claims
    04 / Re-run the same questions and compare results
    Inside the guide
    01
    Graph vs. search decision matrix
    Choose direct lookup, vector search, a graph, a hybrid, or no graph at all.
    02
    One complete worked example
    Follow a fictional launch-risk question from source records to query, answer, correction, and re-evaluation.
    03
    Failure lab
    Practise resolution errors, stale commitments, contradictions, and unsupported edges.
    04
    A small evaluation pack
    Score evidence coverage, relationship correctness, freshness, answer usefulness, and safety.
    Get the full guide

    Build your first graph slice

    Enter your email for the implementation guide. It is designed to be used at a desk, with one real question open.

    The operating model

    A useful graph lives inside a larger loop.

    01

    Connected memory

    Link people, projects, decisions, commitments, and source records without flattening their history.

    02

    Daily operations

    Use the relationship layer in reviews, preparation, triage, and the next-action loop.

    03

    Evidence-gated learning

    Promote a pattern only after repeated signals, a named source, an owner, and a review decision.

    04

    Task truth

    Connect work items to the commitments and decisions they affect. Keep active task state in the task system.

    05

    Honest automation

    Package a proven workflow after the manual loop works. Keep runtime state and approval boundaries visible.

    Second Brain 2.1 reference map

    Graph engineering gives the operating brain a relationship layer.

    The wider Second Brain 2.1 model adds current truth, rituals, task truth, provider routing, evidence-gated learning, and maintenance. This guide shows where a graph helps and where a simpler record is safer.

    Current truth with confidence and last-confirmed fields
    Begin, end, daily, and weekly review rituals
    Corrections, learning candidates, and approval gates
    Stale facts, contradictions, duplicates, and orphan checks
    GitHub Issues as the active work backbone
    Provider, runtime, and automation state
    Working rule

    If you cannot name the source, the relationship should not quietly become truth.

    What you get

    A practical starting pack

    01Question-first design canvas
    02Graph, vector, hybrid, and no-graph matrix
    03Synthetic worked example with queries
    04Graph-lite schema with provenance fields
    05Failure lab and recovery paths
    06Scored evaluation sheet
    Iwo Szapar
    Why I made this

    Most graph tutorials begin with nodes. This one begins with a question you need to answer.

    The goal is a reliable slice you can inspect, correct, and compare against a simpler baseline. The examples are synthetic where stated. Product capabilities and provider availability vary by setup, so the guide keeps claims and architecture boundaries explicit.

    Give your knowledge system better connections.

    Start with one question. Add only the relationships that make the answer more complete, more explainable, or easier to maintain.

    See Second Brain