Design full-stack LLM applications with routing, retrieval, tooling, and safety layers
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Map ingestion, embeddings, vector stores, and LLM orchestration for grounded generation
Describe your agent and the AI draws the loop — model, tools, memory, guardrails, and the humans and systems it touches
Add safety layers to LLMs with content filters, policies, approvals, and audit logs
Common questions about ai llm application architecture generator
Add a router using classifiers, heuristics, or rules to pick models by task type, latency budget, or cost. Show fallbacks for errors.
Include a tool registry, execution sandbox, and observation store. Diagram LLM → tool call → result → follow-up prompt.
Add input/output moderation, prompt validation, content filters, and jailbreak detection. Include audit logs and red-teaming flows.
Combine system prompts, user history, retrieved context, and tool outputs. Show memory stores and context window management.
Track latency, cost, token usage, safety violations, user feedback, and guardrail blocks. Include tracing for each turn.
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