LLMs have a training cutoff date — they only know what existed when they were trained. They cannot access your private or sensitive business data, company workflows, or internal documentation. Without RAG, you get generic answers, hallucinations, and zero domain specificity. RAG solves this by letting you inject your own knowledge at query time.

The pipeline flows: Document → Chunking → Embedding → Vector DB → Query → LLM → Response. Chunking splits documents into token-sized pieces — by sentence, paragraph, or semantic boundary. Embedding models convert those chunks into numerical vectors. A vector database stores them and retrieves the most relevant chunks using distance algorithms like cosine similarity.

Cloud embedding models (OpenAI, Google, Anthropic) handle multi-modal content automatically — text, image, audio. Single-purpose local models (Ollama, LM Studio) are cheaper and faster for specific tasks. Vector databases store embeddings and retrieve by distance algorithms. The choice depends on your scale, privacy needs, and budget.

Before vibe coding, prepare your knowledge documents. Markdown files are preferred over PDF — fewer tokens, cleaner parsing. Use Gemini Gems or GPT assistants to create persistent knowledge spaces. Feed business workflows, competitor analysis, and domain documentation. Quality in equals quality out: RAG knowledge determines output quality. Remember: regular chat is session-based (in-memory only, gone when closed). Persistent knowledge survives across sessions.

MCP (Model Context Protocol) is a standard protocol by Anthropic for AI-to-system communication. It uses a server/client architecture: MCP servers expose tools, and AI clients call them. Think of it as API endpoints specifically designed for AI agents to discover and use autonomously. MCP is built into Claude Code and configurable in settings as MCP servers.

You can build custom MCP servers that define specific tools: list templates, connect accounts, publish to blog/Facebook/LinkedIn. AI calls these tools during workflow — no manual copy-paste. MCP integrates with any system: Gmail, Calendar, Google Drive, custom APIs. The key difference from regular APIs: MCP defines tool schemas that AI can discover and call autonomously.

Security is critical: scope permissions carefully using the principle of least privilege. Real risk exists — overpermissioned agents can cause rm -rf incidents. Example: Gmail MCP should not have delete permission if you only need read access. Always define exact actions the MCP can perform. Human-in-the-loop serves as a safety net: approve destructive actions before execution.

The agentic loop follows: Plan → Execute → Evaluate → Correct → Repeat. AI creates a plan, attempts execution, evaluates results, fixes errors, and loops until done. The key difference from basic chat: autonomous multi-step problem solving. Instead of one question/one answer, the agent orchestrates an entire workflow.

AI is not fully autonomous — humans must be the final checkpoint. Quality control means human reviews AI output before publishing or deploying. Example: in a content system, AI writes the article and human approves before posting. Without human-in-the-loop: risk of garbage output, accidental data deletion, or security breaches. The human decides WHAT to do; the agent decides HOW.

Know your models: use Opus for complex reasoning, Sonnet for everyday coding, Haiku for simple tasks, Gemini Flash for brainstorming. Early phase: use expensive models to build quality memory and history. Later phase: switch to cheaper or local models that reference stored knowledge. Open Router provides a single API to test multiple models and compare cost vs quality. Convert repeated MCP workflows to skills to save tokens. Periodically audit token usage and remove redundant patterns.