Frequently asked questions

A Brain API is an interface that exposes “AI brain” capabilities—like memory, retrieval, prompt programs, and tool use—so your app can call them like standard API functions. Instead of wiring an LLM directly into each feature, a Brain API acts as a shared intelligence layer: it can remember user context, retrieve knowledge from approved sources, and produce structured outputs your product can reliably consume. This approach helps you build AI features that compound over time, rather than resetting every session.

A chat integration is usually a single request/response loop with limited persistence. A Brain API is a system, not a UI: it supports long-term memory, permission-aware retrieval (RAG), reusable prompt templates, tool calling, and governance (logs, rate limits, confirmations). That means you can build multiple product features—support, drafting, analytics, automation—on one consistent “brain” layer, while controlling behavior and observing quality over time.

RAG (Retrieval-Augmented Generation) is a method that retrieves relevant documents (or snippets) and provides them to the model at runtime so the response is grounded in evidence. Brain APIs rely on RAG because it improves accuracy, supports recency (new docs can be indexed), and enables traceability via citations. It also helps reduce hallucinations by encouraging the model to answer from retrieved sources, or to say “not found” when evidence is missing.

Most AI brain systems separate memory into layers: short-term (the current task/session) and long-term (persistent preferences, facts, project context). Many implementations also use scopes: user memory (personal), team memory (shared), and org memory (policies, official docs). Good memory design is explicit and editable: users should know what’s stored, how it’s used, and how to delete or update it—especially when memory influences automated actions.

In Brain API systems, prompts are treated like reusable programs, not one-off strings. A strong prompt includes goals, constraints, tool rules, and output schemas (like JSON). Teams version prompts, test them with evaluation suites, and roll back when behavior drifts—similar to code. This makes AI output more consistent across endpoints and model upgrades, which is critical when your app depends on structured data or safe tool execution.

LLM integrations connect your Brain API layer to one or more model providers. Model routing chooses the best model for each task (fast drafting vs deep reasoning vs code vs structured extraction), based on constraints like latency, cost, and risk level. This matters because no single model is best at everything, and providers evolve quickly. A Brain API can keep your app stable while improving underlying model performance over time.

Yes—Brain APIs commonly integrate with tools like databases, CRMs, ticketing systems, and document stores. The safest pattern is to wrap tools behind strict schemas, least privilege permissions (read-only by default), and confirmations for any action that changes state. This lets the AI “do work” (fetch facts, draft updates, propose actions) while keeping humans in control of high-impact operations.

A production Brain API typically uses several layers of protection: permission-aware retrieval (ACL filtering), tenant isolation, redaction for sensitive fields, safe logging, and tool constraints (least privilege + confirmations). Prompts can also enforce policy (“don’t reveal secrets,” “cite sources,” “refuse restricted requests”), but the strongest safety comes from system-level controls—especially around tools and data access.

“Databases as AI” means your data becomes queryable by intent, not just by exact schema knowledge. Practically, it often combines: (1) RAG over unstructured documents, (2) safe tool access to structured databases (sometimes NL2SQL with guardrails), and (3) a semantic layer (entities, metric definitions, synonyms). The goal is for users to ask natural questions and receive evidence-backed answers, with clear provenance and permissions.

Start with one high-value workflow and build it end-to-end: ingest a small doc set, implement permission-aware retrieval, define a prompt template with an output schema, and add basic observability (logs + metrics). Then expand: add memory scopes, tool integrations, and evaluations so changes don’t break behavior. The key is to treat the “brain” as a product platform—versioned, tested, and governed—rather than a single chat feature. --- #AI #LLM #BrainsAPI #BrainAPI