{"id":4021,"date":"2026-08-19T10:00:00","date_gmt":"2026-08-19T14:00:00","guid":{"rendered":"https:\/\/www.mymiller.name\/wordpress\/?p=4021"},"modified":"2026-08-16T08:31:43","modified_gmt":"2026-08-16T12:31:43","slug":"spec-driven-ai-architecture-designing-applications-with-app_spec-md-design-md-agents-md-and-adrs","status":"publish","type":"post","link":"https:\/\/www.mymiller.name\/wordpress\/spring_ai\/spec-driven-ai-architecture-designing-applications-with-app_spec-md-design-md-agents-md-and-adrs\/","title":{"rendered":"Spec-Driven AI Architecture: Designing Applications with APP_SPEC.md, DESIGN.md, AGENTS.md, and ADRs"},"content":{"rendered":"\n

In the era of AI-assisted software engineering, traditional high-level architecture diagrams and scattered wiki pages are no longer sufficient. When human developers and AI coding agents (such as Claude Code, Cursor, Windsurf, or Aider) collaborate on a codebase, the architecture must be explicit, machine-readable, version-controlled, and deterministic<\/strong>.<\/p>\n\n\n\n

This document outlines a modern architectural pattern called Spec-Driven AI Architecture<\/strong>, built around four core markdown artifacts:<\/p>\n\n\n\n

    \n
  1. APP_SPEC.md<\/code><\/strong>: The functional product specification defining what<\/em> is being built from a business and feature standpoint.<\/li>\n\n\n\n
  2. DESIGN.md<\/code><\/strong>: The technical system design detailing how<\/em> systems interact, component hierarchies, sequence flows, and internal contracts.<\/li>\n\n\n\n
  3. AGENTS.md<\/code><\/strong>: The operational guardrails defining how<\/em> AI agents must write, test, structure, and validate code.<\/li>\n\n\n\n
  4. doc\/adr\/*.md<\/code> (Architectural Decision Records)<\/strong>: The immutable history detailing why<\/em> specific technical choices and trade-offs were made.<\/li>\n<\/ol>\n\n\n\n

    1. The Four-Pillar Architecture Framework<\/h2>\n\n\n\n
                     +-----------------------+\n                 |     APP_SPEC.md       |\n                 | (Functional Spec,     |\n                 |  Features & Product)  |\n                 +-----------+-----------+\n                             |\n                             v\n                 +-----------------------+\n                 |       DESIGN.md       |\n                 | (Technical System     |\n                 |  Design & Workflows)  |\n                 +-----------+-----------+\n                             |\n         +-------------------+-------------------+\n         |                                       |\n         v                                       v\n+-----------------------+               +-----------------------+\n|      AGENTS.md        | <-----------> |    doc\/adr\/*.md       |\n| (Execution Rules,     |  References   | (Architecture Records,|\n| Code Quality & Stack) |   Constraints | Trade-offs & History) |\n+-----------------------+               +-----------------------+\n<\/code><\/pre>\n\n\n\n
    Roles and Responsibilities<\/h3>\n\n\n\n
    Artifact<\/strong><\/td>Audience<\/strong><\/td>Primary Purpose<\/strong><\/td>Key Content<\/strong><\/td><\/tr>
    APP_SPEC.md<\/code><\/strong><\/td>Product & Engineering<\/td>Product Blueprint<\/td>User personas, functional scope, feature requirements, high-level user stories<\/td><\/tr>
    DESIGN.md<\/code><\/strong><\/td>Engineering & AI Agents<\/td>Technical Blueprint<\/td>Component topology, sequence diagrams, class\/interface models, internal data flows<\/td><\/tr>
    AGENTS.md<\/code><\/strong><\/td>AI Agents<\/td>Execution Guardrails<\/td>File structure rules, library choices, forbidden patterns, testing commands<\/td><\/tr>
    ADR (000x-*.md<\/code>)<\/strong><\/td>Humans & AI<\/td>Rationale & Context<\/td>Historical decision context, trade-offs, accepted consequences<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    2. Defining Product Requirements (APP_SPEC.md<\/code>)<\/h2>\n\n\n\n
    APP_SPEC.md<\/code> serves as the functional source of truth. It translates business intent into actionable engineering scope without getting bogged down in low-level code implementation details.<\/p>\n\n\n\n
    Key Sections of a Production-Grade APP_SPEC.md<\/code><\/h3>\n\n\n\n
      \n
    1. Executive Overview<\/strong>: Core business objective and target audience.<\/li>\n\n\n\n
    2. Feature Scope & Capabilities<\/strong>: Functional requirements grouped by domain.<\/li>\n\n\n\n
    3. User Experience Goals<\/strong>: Primary navigation flows and user journey steps.<\/li>\n\n\n\n
    4. Non-Functional Requirements<\/strong>: Performance targets, security standards, and accessibility expectations.<\/li>\n<\/ol>\n\n\n\n
      Template: APP_SPEC.md<\/code><\/h3>\n\n\n\n
      # Application Specification: Document Management System\n\n## 1. Product Overview\n- **Name**: DocuFlow\n- **Type**: Web Application (REST API + SPA)\n- **Goal**: Enable teams to securely index, search, and analyze corporate documents using AI assistance.\n\n## 2. Functional Requirements\n### FR-1: Document Ingestion\n- Users can upload PDF and Markdown files up to 25MB.\n- The system must automatically parse and index uploaded documents.\n\n### FR-2: Document Search & Retrieval\n- Users can perform full-text and semantic search.\n- Search results must display document title, status, creation date, and relevancy score.\n\n### FR-3: AI Summarization\n- Users can request an AI-generated summary of selected documents.\n- Summarization must execute using strictly local inference engines to protect sensitive data.\n\n## 3. User Experience & Flows\n1. **Dashboard**: Displays recent documents and upload trigger.\n2. **Document Viewer**: Split pane with document preview on the left and AI chat\/summary on the right.\n<\/code><\/pre>\n\n\n\n
      3. Defining Technical System Architecture (DESIGN.md<\/code>)<\/h2>\n\n\n\n
      While APP_SPEC.md<\/code> describes what<\/em> the product does, DESIGN.md<\/code> specifies how<\/em> the software is technically constructed. It serves as the architectural blueprint that AI agents consult to understand service interactions, interface designs, state machines, and data schemas.<\/p>\n\n\n\n
      Key Sections of DESIGN.md<\/code><\/h3>\n\n\n\n
        \n
      1. System Topology & Component Diagram<\/strong>: High-level structural organization.<\/li>\n\n\n\n
      2. Detailed API & Service Contracts<\/strong>: Endpoint definitions, request\/response models, and status codes.<\/li>\n\n\n\n
      3. Data Schemas & Persistence<\/strong>: Database models, indexing strategies, and relationships.<\/li>\n\n\n\n
      4. Sequence & Workflow Diagrams<\/strong>: ASCII or Mermaid diagrams detailing step-by-step execution.<\/li>\n\n\n\n
      5. Frontend Component Architecture<\/strong>: View hierarchy, state management strategy, and UI library binding.<\/li>\n<\/ol>\n\n\n\n
        Template: DESIGN.md<\/code><\/h3>\n\n\n\n
        # Technical System Design: DocuFlow\n\n## 1. High-Level Component Topology\n<\/code><\/pre>\n\n\n\n
        [ Angular SPA ] \u2500\u2500(HTTP\/REST)\u2500\u2500> [ Spring Boot API Gateway ]<\/p>\n\n\n\n
        \u2502<\/p>\n\n\n\n
        \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510<\/p>\n\n\n\n
        \u25bc \u25bc<\/p>\n\n\n\n
        [ Document Service ] [ AI Indexer Service ]<\/p>\n\n\n\n
        \u2502 \u2502<\/p>\n\n\n\n
        \u25bc \u25bc<\/p>\n\n\n\n
        (PostgreSQL \/ JPA) (Spring AI + Ollama)<\/p>\n\n\n\n
        \n## 2. Data Models & Database Schemas\n\n### 2.1 Schema: `documents`\n| Field | Type | Constraints | Description |\n| :--- | :--- | :--- | :--- |\n| `id` | UUID | Primary Key | Unique document ID |\n| `title` | VARCHAR(255) | NOT NULL | Document title |\n| `status` | VARCHAR(32) | NOT NULL | Enum: `DRAFT`, `PROCESSING`, `PUBLISHED` |\n| `created_at` | TIMESTAMP WITH TIMEZONE | NOT NULL | Creation timestamp |\n\n## 3. REST API Contracts\n\n### POST \/api\/v1\/documents\n- **Description**: Upload and initiate parsing for a new document.\n- **Request Body**: `MultipartFile file`, `String title`\n- **Response**: `201 Created`\n  ```json\n  {\n    \"id\": \"123e4567-e89b-12d3-a456-426614174000\",\n    \"title\": \"Quarterly Report\",\n    \"status\": \"PROCESSING\",\n    \"createdAt\": \"2026-03-15T10:00:00Z\"\n  }\n<\/code><\/pre>\n\n\n\n
        GET \/api\/v1\/documents<\/h3>\n\n\n\n
          \n
        • Description<\/strong>: Search and list documents.<\/li>\n\n\n\n
        • Query Parameters<\/strong>: page<\/code> (int), size<\/code> (int), status<\/code> (String)<\/li>\n\n\n\n
        • Response<\/strong>: 200 OK<\/code> (Page<DocumentDTO><\/code>)<\/li>\n<\/ul>\n\n\n\n
          4. Sequence Diagram: Document Processing<\/h2>\n\n\n\n
          User             Angular UI            Document Controller       Spring AI (Ollama)\n \u2502                   \u2502                          \u2502                          \u2502\n \u251c\u2500\u2500 Upload File \u2500\u2500\u2500>\u2502                          \u2502                          \u2502\n \u2502                   \u251c\u2500\u2500 POST \/api\/v1\/docs \u2500\u2500\u2500\u2500>\u2502                          \u2502\n \u2502                   \u2502                          \u251c\u2500\u2500 Generate Embedding \u2500\u2500\u2500>\u2502\n \u2502                   \u2502                          \u2502<\u2500\u2500 Embedding Vector \u2500\u2500\u2500\u2500\u2500\u2524\n \u2502                   \u2502                          \u251c\u2500\u2500 Save Entity & Vector   \u2502\n \u2502                   \u2502<\u2500\u2500 201 Created (DTO) \u2500\u2500\u2500\u2500\u2500\u2500\u2524                          \u2502\n<\/code><\/pre>\n\n\n\n
          5. Frontend UI Component Hierarchy<\/h2>\n\n\n\n
            \n
          • AppComponent<\/code> (Root layout with MatSidenav<\/code>)\n
              \n
            • DocumentListComponent<\/code> (Displays data via Angular Material MatTable<\/code>)<\/li>\n\n\n\n
            • DocumentUploadDialogComponent<\/code> (Material MatDialog<\/code> form for uploads)<\/li>\n\n\n\n
            • DocumentDetailComponent<\/code> (Split viewer using Signals for reactive state)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
              \n---\n\n## 4. Guiding the AI Developer (`AGENTS.md`)\n\n`AGENTS.md` governs *how* AI agents operate inside your repository. It enforces technical stack selections, coding standards, prohibited patterns, and test execution procedures.\n\n### Core Objectives of `AGENTS.md`\n\n- **Enforce Architectural Boundaries**: Prevent agents from mixing presentation, business logic, or data access layers.\n- **Lock Down Technology Choices**: Enforce specific tooling (e.g., Gradle build scripts, Angular Material components, Spring AI modules).\n- **Define Code Quality Rules**: Enforce DTO encapsulation, parameter usage rules, and immutability.\n- **Provide Verification Commands**: Detail command-line instructions for build and test validation.\n\n### Template: `AGENTS.md`\n\n```markdown\n# Agent Directives & Repository Rules\n\n## 1. Role & Behavior\nYou are an expert Principal Software Architect and Developer.\nBefore generating or modifying code, inspect `APP_SPEC.md`, `DESIGN.md`, and relevant ADRs in `doc\/adr\/`.\nNever alter core architectural patterns without creating a new ADR proposal.\n\n## 2. Technology Stack & Tooling Constraints\n- **Build Tool**: Gradle ONLY (Do not generate or edit `pom.xml` files).\n- **Backend**: Java 21+, Spring Boot 3.x, Spring Data JPA.\n- **Frontend**: Angular 18+, Angular Material UI components.\n- **AI\/LLM Integrations**: Spring AI modules routed to local Ollama instances.\n\n## 3. Architectural Rules & Standards\n### Backend (Spring Boot)\n- **Layering**: Controller -> Service -> Repository. Controllers must never access Repositories directly.\n- **REST Protocol Rules**:\n  - `POST` and `PUT` request payloads MUST use strongly-typed `@RequestBody` DTOs.\n  - Query parameters (`@RequestParam`) are strictly reserved for search, pagination, and filter parameters on `GET` requests.\n- **Persistence**: Never expose JPA entities over REST endpoints. Always convert entities to DTOs in the Service layer.\n\n### Frontend (Angular)\n- **UI Components**: Use Angular Material primitives (`MatButtonModule`, `MatTableModule`, `MatSidenavModule`, etc.).\n- **State Management**: Prefer Angular Signals for local state management and RxJS for asynchronous stream handling.\n- **Structure**: Feature-based folder organization (`\/src\/app\/features\/documents\/...`).\n\n## 4. Testing & Verification Requirements\nBefore marking any task as complete, execute and verify using:\n- `.\/gradlew test` (Backend validation)\n- `ng test --watch=false` (Frontend validation)\n\n## 5. Prohibited Actions\n- DO NOT add external dependencies to `build.gradle` without explicit permission.\n- DO NOT hardcode credentials, tokens, or local file paths.\n- DO NOT remove or bypass failing unit tests.\n<\/code><\/pre>\n\n\n\n
              5. Recording Architecture Decision Records (ADRs)<\/h2>\n\n\n\n
              Architectural Decision Records capture significant technical choices, including their context and trade-offs.<\/p>\n\n\n\n
              In AI-driven development, ADRs are critical guardrails<\/strong>. AI agents lack persistent memory across distinct sessions. Without recorded ADRs, an agent might inadvertently refactor a deliberate trade-off (e.g., replacing a local Ollama integration with a cloud API, or switching build systems).<\/p>\n\n\n\n
              Where ADRs Live<\/h3>\n\n\n\n
              Store ADRs in the repository under a structured directory:<\/p>\n\n\n\n
              \/doc\n  \/adr\n    0001-record-architecture-decisions.md\n    0002-use-spring-boot-and-gradle.md\n    0003-use-angular-material-for-ui.md\n    0004-use-spring-ai-with-ollama.md\n<\/code><\/pre>\n\n\n\n
              Standard ADR Format (MADR \/ Nygard Format)<\/h3>\n\n\n\n
              Every ADR should follow a strict, lightweight Markdown schema:<\/p>\n\n\n\n
                \n
              1. Title<\/strong>: Sequentially numbered decision (e.g., 0004-use-spring-ai-with-ollama.md<\/code>).<\/li>\n\n\n\n
              2. Status<\/strong>: Proposed, Accepted, Rejected, Deprecated, or Superseded.<\/li>\n\n\n\n
              3. Context<\/strong>: What problem are we solving? What constraints exist?<\/li>\n\n\n\n
              4. Decision<\/strong>: What choice was made?<\/li>\n\n\n\n
              5. Consequences<\/strong>: What becomes easier or harder as a result?<\/li>\n<\/ol>\n\n\n\n
                Template: Architecture Decision Record<\/h3>\n\n\n\n
                # [Number]. [Short Title of Decision]\n\n* **Status**: [Proposed | Accepted | Superseded by ADR-XXXX]\n* **Date**: YYYY-MM-DD\n* **Deciders**: [Architect \/ Tech Lead Name(s)]\n\n## Context and Problem Statement\n[Describe the technical context, business requirement, or problem requiring an architectural decision. Keep it concise.]\n\n## Decision Drivers\n* [Driver 1: e.g., Local data privacy constraints]\n* [Driver 2: e.g., Team expertise in Java and Angular]\n* [Driver 3: e.g., Low latency requirements]\n\n## Considered Options\n1. [Option 1]\n2. [Option 2]\n3. [Option 3]\n\n## Decision Outcome\nChosen Option: **[Option X]**, because [brief justification].\n\n### Positive Consequences\n* [Benefit 1]\n* [Benefit 2]\n\n### Negative Consequences \/ Trade-offs\n* [Drawback or operational complexity accepted]\n* [Maintenance burden accepted]\n\n## Pros and Cons of Options\n\n### [Option 1]\n* Good, because [...]\n* Bad, because [...]\n\n### [Option 2]\n* Good, because [...]\n* Bad, because [...]\n<\/code><\/pre>\n\n\n\n
                Sample ADR: Integrating Local LLMs<\/h3>\n\n\n\n
                # 0004. Use Spring AI with Ollama for Local Inference\n\n* **Status**: Accepted\n* **Date**: 2026-03-15\n* **Deciders**: Lead Architect\n\n## Context and Problem Statement\nThe application requires intelligent document indexing and semantic extraction. Data privacy policies prohibit sending internal document content to third-party public cloud LLM endpoints.\n\n## Decision Drivers\n* Strict data sovereignty and zero external telemetry requirements.\n* Need for seamless integration with our Spring Boot infrastructure.\n* Reproducible local developer environment.\n\n## Considered Options\n1. Cloud-hosted LLM APIs (OpenAI, Anthropic) via direct REST calls.\n2. Spring AI integrated with local Ollama instances.\n3. Custom Python sidecar microservice running HuggingFace models.\n\n## Decision Outcome\nChosen Option: **Spring AI with Ollama**, because Spring AI provides standard abstractions inside our existing Java ecosystem while Ollama manages local model lifecycles securely inside our deployment perimeter.\n\n### Positive Consequences\n* No sensitive document data leaves the host infrastructure.\n* Standardized `ChatModel` interface allows swapping local models easily.\n* Native Java configuration via `build.gradle` without Python sidecars.\n\n### Negative Consequences \/ Trade-offs\n* Developer machines must have sufficient GPU\/RAM resources to run Ollama locally.\n<\/code><\/pre>\n\n\n\n
                6. The Integrated End-to-End Workflow<\/h2>\n\n\n\n
                How do APP_SPEC.md<\/code>, DESIGN.md<\/code>, AGENTS.md<\/code>, and ADRs interact in day-to-day development?<\/p>\n\n\n\n
                 [1. Product\/Architect] \u2500\u2500> Drafts APP_SPEC.md (Functional Scope)\n                                  \u2502\n                                  \u25bc\n [2. Tech Architect]    \u2500\u2500> Drafts DESIGN.md & Initial ADRs\n                                  \u2502\n                                  \u25bc\n [3. Agent Execution]   \u2500\u2500> Reads APP_SPEC.md + DESIGN.md + AGENTS.md + ADRs\n                                  \u2502\n                                  \u25bc\n [4. Implementation]    \u2500\u2500> Generates code adhering to specs & constraints\n                                  \u2502\n                                  \u25bc\n [5. Verification]      \u2500\u2500> Runs Gradle & Angular test suites (per AGENTS.md)\n                                  \u2502\n                                  \u25bc\n [6. Architectural Change?] \u2500\u2500 YES \u2500\u2500> Drafts new ADR (000X-title.md)\n          \u2502\n          \u2514\u2500\u2500 NO \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500> Merges Code & Updates DESIGN.md if needed\n<\/code><\/pre>\n\n\n\n
                Step-by-Step Execution Protocol<\/h3>\n\n\n\n
                  \n
                1. Functional Definition<\/strong>:\n
                    \n
                  • The Product Manager or Architect writes APP_SPEC.md<\/code> defining key features and user flows.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                  • Technical Blueprinting<\/strong>:\n
                      \n
                    • The Software Architect creates DESIGN.md<\/code> defining data schemas, API payload structures, sequence flows, and Angular Material component layouts.<\/li>\n\n\n\n
                    • Initial foundational choices (frameworks, build tools, database setups) are logged in doc\/adr\/<\/code>.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                    • Agent Directives Setup<\/strong>:\n
                        \n
                      • AGENTS.md<\/code> is verified or updated to enforce repository rules (e.g., Gradle commands, Spring Boot DTO patterns, testing instructions).<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                      • Agent Prompting Phase<\/strong>:\n
                          \n
                        • Prompt the AI coding agent with explicit artifact references:“Agent, review APP_SPEC.md<\/code> Section 2 (FR-1) and DESIGN.md<\/code> Section 3. Implement the POST \/api\/v1\/documents<\/code> endpoint using Spring Boot DTOs and Angular Material frontend upload components. Follow all rules in AGENTS.md<\/code> and check doc\/adr\/0004<\/code>.”<\/em><\/li>\n<\/ul>\n<\/li>\n\n\n\n
                        • Validation & ADR Evolution<\/strong>:\n
                            \n
                          • The agent runs .\/gradlew test<\/code> and ng test --watch=false<\/code> as instructed in AGENTS.md<\/code>.<\/li>\n\n\n\n
                          • If a new structural trade-off arises during execution, a new ADR is recorded in doc\/adr\/<\/code> before committing architectural updates.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n
                            7. Summary Checklist for Teams<\/h2>\n\n\n\n
                            To establish a successful Spec-Driven AI Architecture, ensure your repository contains:<\/p>\n\n\n\n
                              \n
                            • [ ] APP_SPEC.md<\/code><\/strong>: Product blueprint defining features, user flows, and functional requirements.<\/li>\n\n\n\n
                            • [ ] DESIGN.md<\/code><\/strong>: Technical blueprint defining schemas, sequence diagrams, API contracts, and component trees.<\/li>\n\n\n\n
                            • [ ] AGENTS.md<\/code><\/strong>: AI execution directives detailing stack rules, styling guidelines, forbidden practices, and test commands.<\/li>\n\n\n\n
                            • [ ] doc\/adr\/<\/code><\/strong>: Directory containing sequentially numbered Architecture Decision Records.<\/li>\n\n\n\n
                            • [ ] Automated CI\/CD<\/strong>: Integration pipelines that execute tests specified in AGENTS.md<\/code>.<\/li>\n<\/ul>\n\n\n\n
                              By combining functional scope (APP_SPEC.md<\/code>)<\/strong>, technical architecture (DESIGN.md<\/code>)<\/strong>, execution guardrails (AGENTS.md<\/code>)<\/strong>, and decision history (ADRs)<\/strong>, teams achieve a clear, repeatable workflow for both human software engineers and AI coding agents.<\/p>\n","protected":false},"excerpt":{"rendered":"
                              In the era of AI-assisted software engineering, traditional high-level architecture diagrams and scattered wiki pages are no longer sufficient. When human developers and AI coding agents (such as Claude Code, Cursor, Windsurf, or Aider) collaborate on a codebase, the architecture must be explicit, machine-readable, version-controlled, and deterministic. This document outlines a modern architectural pattern called […]<\/p>\n","protected":false},"author":1,"featured_media":4025,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[495,443],"tags":[],"series":[],"class_list":["post-4021","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-spring_ai"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/www.mymiller.name\/wordpress\/wp-content\/uploads\/2026\/08\/1786883436935.avif","jetpack-related-posts":[{"id":4004,"url":"https:\/\/www.mymiller.name\/wordpress\/ai\/the-complete-guide-to-modern-ai-terminology-from-neural-networks-to-rag-mcp-and-agentic-systems\/","url_meta":{"origin":4021,"position":0},"title":"The Complete Guide to Modern AI Terminology: From Neural Networks to RAG, MCP, and Agentic Systems","author":"Jeffery Miller","date":"July 29, 2026","format":false,"excerpt":"Artificial Intelligence is evolving rapidly, bringing with it a wave of new concepts, acronyms, and technical jargon. Whether you are building AI applications, reading tech news, or evaluating tools for work, understanding this vocabulary is essential. 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It requires handling complex state, real-time networking, concurrency, and deep game logic. Usually, this takes months of meticulous, line-by-line keyboard grinding. But for AIMUD, we didn't just code; we vibe coded. 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