Integrating LLMs with External Data & Tools
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This deck distills the key concepts, patterns, protocols, and pain points involved in wiring Large Language Models (LLMs) to real‑world data sources and external tools.
1. Why do LLM-powered apps need *structured* external context?
• Model weights are frozen; they lack up‑to‑date or private data.
• Structured context lets the app feed fresh facts or user‑specific info and invoke actions safely instead of relying on hallucinations.
2. Define Retrieval‑Augmented Generation (RAG).
Retrieve relevant documents (via search / vector DB), prepend them to the prompt, then generate—grounding the LLM’s answer in authoritative sources.
3. Give one benefit of RAG over finetuning.
Content can be updated instantly without retraining the model.
4. What is tool calling in the LLM context?
The model outputs a structured request (e.g. JSON) to invoke an external API/function; the orchestrator executes it and returns the result for the model to incorporate.
5. Early ad‑hoc pattern for tool use?
Bespoke REST hooks—regex or prompt tricks mapping certain phrases to API calls; brittle and hard to maintain.
6. Key idea behind ChatGPT‑style plugins?
Publish an OpenAPI spec + .well‑known manifest so ChatGPT can call an external web service through standardized endpoints.
7. How does LangChain’s *agent* pattern work?
Prompt → LLM proposes Action / Action Input → framework executes tool → returns Observation → loop continues until Final Answer.
8. OpenAI function‑calling JSON interface—what problem does it solve?
Guarantees tool calls are valid, machine‑parseable JSON conforming to a schema, eliminating fragile text parsing.
9. Default transport & encoding for most integrations?
HTTP + JSON (REST) for ubiquity and human readability.
10. Trade‑off of HTTP+JSON vs gRPC?
HTTP+JSON: easy, verbose, higher latency.
gRPC: binary, fast, strongly‑typed, but less universal and harder for an LLM to emit directly.
11. When are WebSockets valuable in LLM tool use?
Streaming or long‑lived, bidirectional interactions (e.g. live stock prices, multi‑step agent sessions).
12. What does JSON‑RPC 2.0 provide?
• Uniform request/response envelope (jsonrpc, method, params, id).
• Transport‑agnostic structure that eases parsing and multiplexing calls.
13. Core security mechanism for user‑consented access?
OAuth 2.0—user grants scoped tokens; no passwords or long‑lived secrets exposed.
14. Why keep API keys out of the LLM prompt?
The model might inadvertently leak them; keys should stay server‑side or in a secret vault.
15. How do signed URLs embody least privilege?
They grant temporary access to a single resource, expiring automatically.
16. Purpose of scoping and permission boundaries?
Restrict what the AI can read or modify, preventing accidental destructive actions and limiting damage if compromised.
17. Explain rate limiting in AI tool orchestration.
Caps call frequency to protect external services, control costs, and stop runaway loops.
18. Why validate/sanitize the LLM’s tool‑call parameters?
Treat model output like untrusted user input—guard against injections or unsafe operations.
19. Pain point: ecosystem fragmentation—describe it.
Each model/provider had its own plugin or agent format, forcing duplicate integrations that couldn’t interoperate.
20. Pain point: brittle glue code—what causes it?
Reliance on regex/parsing of free‑form text or custom JSON; slightest format drift breaks execution.
21. Pain point addressed by standardizing auth flows?
Removes patchwork of per‑plugin methods, reducing security risk and developer overhead.
22. What is the Model Context Protocol (MCP)?
An open, JSON‑RPC‑based standard (think “USB‑C for AI”) that lets any LLM agent securely invoke any tool/data source via a uniform interface.
23. Two security features MCP bakes in by default?
• OAuth 2.0 handshake for delegated access.
• Consistent permission scoping & token handling across all tools.
24. How does MCP reduce integration overhead?
Plug‑and‑play: implement an MCP server once, reuse across all compliant AI clients—no bespoke adapters.
25. Name three gaps MCP closes.
- Lack of a standard interface for tool calls.
- Inconsistent authentication/security practices.
- Difficulty maintaining stateful, multi‑step workflows across tools.
26. Long‑term benefit of adopting a universal protocol like MCP?
Scalable, maintainable AI ecosystems free from vendor lock‑in, enabling faster innovation and safer, richer agent capabilities.