From Bytes to RAG: Assembling an Agent Knowledge Base

{
  "byte_id": "agentic_dev_022",
  "title": "From Bytes to RAG: Assembling an Agent Knowledge Base",
  "level": "applied",
  "domain": [
    "agentic-development",
    "rag",
    "knowledge-architecture"
  ],
  "intent": "Learn how to turn individual bytes into a coherent RAG knowledge base that agents can reliably use in production.",
  "core_idea": {
    "one_liner": "RAG is not a document store — it is an executable knowledge system.",
    "why_it_matters": [
      "Random notes do not become intelligence automatically.",
      "Agents need predictable retrieval and reasoning paths.",
      "Well-structured bytes scale across agents and use cases."
    ]
  },
  "knowledge_layers": [
    {
      "layer": "Foundations",
      "purpose": "How agents think and behave.",
      "content": [
        "agent loop",
        "guardrails",
        "planning",
        "verification"
      ]
    },
    {
      "layer": "Decision bytes",
      "purpose": "When to choose one option over another.",
      "content": [
        "when-to-use rules",
        "trade-offs",
        "constraints"
      ]
    },
    {
      "layer": "Operational bytes",
      "purpose": "How to execute safely.",
      "content": [
        "checklists",
        "playbooks",
        "fallbacks"
      ]
    },
    {
      "layer": "Diagnostics bytes",
      "purpose": "Why something is broken.",
      "content": [
        "RCA patterns",
        "symptoms → causes"
      ]
    }
  ],
  "assembly_steps": [
    "Normalize all bytes to a common schema.",
    "Add mandatory metadata (domain, type, version).",
    "Chunk by semantic unit (one byte = one chunk).",
    "Index with embeddings + metadata filters.",
    "Define retrieval rules per agent intent."
  ],
  "retrieval_by_intent": [
    {
      "intent": "decision",
      "preferred_types": [
        "decision",
        "constraint"
      ],
      "fallback_types": [
        "concept"
      ]
    },
    {
      "intent": "how_to",
      "preferred_types": [
        "checklist",
        "playbook"
      ],
      "fallback_types": [
        "decision"
      ]
    },
    {
      "intent": "diagnose",
      "preferred_types": [
        "RCA"
      ],
      "fallback_types": [
        "anti-pattern"
      ]
    }
  ],
  "micro_example": {
    "scenario": "Agent asked: 'How should I handle low-confidence output?'",
    "retrieval": [
      "agentic_dev_008 (Self-check)",
      "agentic_dev_017 (Fallbacks)"
    ],
    "result": "Agent proposes verification + human-in-the-loop."
  },
  "failure_modes": [
    "Mixing unrelated byte types",
    "No intent-based retrieval",
    "Overfetching too many chunks",
    "No version governance"
  ],
  "guards": [
    "One byte = one retrievable unit.",
    "Agents must declare retrieval intent.",
    "RAG responses must cite byte IDs."
  ],
  "teach_it_in_english": {
    "simple_explanation": "You build a library where every card has a clear purpose.",
    "one_sentence_definition": "A good RAG system is a curated map of decisions, not a pile of text."
  },
  "practical_checklist": [
    "Can each byte answer a specific question?",
    "Is retrieval intent explicit?",
    "Are bytes reusable across agents?",
    "Can we explain why a byte was used?"
  ],
  "tags": [
    "rag-assembly",
    "knowledge-base",
    "agent-design",
    "scalability"
  ],
  "meta": {
    "schema": "dkharlanau.dataset.byte",
    "schema_version": "1.1",
    "dataset": "agentic-bytes",
    "source_project": "cv-ai",
    "source_path": "agentic-bytes/agentic_dev_022.json",
    "generated_at_utc": "2026-02-03T14:33:32+00:00",
    "creator": {
      "name": "Dzmitryi Kharlanau",
      "role": "SAP Lead",
      "website": "https://dkharlanau.github.io",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "attribution": {
      "attribution_required": true,
      "preferred_citation": "Dzmitryi Kharlanau (SAP Lead). Dataset bytes: https://dkharlanau.github.io"
    },
    "license": {
      "name": "",
      "spdx": "",
      "url": ""
    },
    "links": {
      "website": "https://dkharlanau.github.io",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "contact": {
      "preferred": "linkedin",
      "linkedin": "https://www.linkedin.com/in/dkharlanau"
    },
    "canonical_url": "https://dkharlanau.github.io/datasets/agentic-bytes/agentic_dev_022.json",
    "created_at_utc": "2026-02-03T14:33:32+00:00",
    "updated_at_utc": "2026-02-03T15:29:02+00:00",
    "provenance": {
      "source_type": "chat_export_extraction",
      "note": "Extracted and curated by Dzmitryi Kharlanau; enriched for attribution and crawler indexing."
    },
    "entity_type": "agentic_byte",
    "entity_subtype": "level:applied",
    "summary": "Learn how to turn individual bytes into a coherent RAG knowledge base that agents can reliably use in production."
  }
}