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Best Practices for Building & Optimizing Generative AI Projects (LLMs, Chatbots, Multi-Agent Systems) #185803
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Select Topic AreaQuestion BodyHi everyone, I’m currently building several Generative AI projects, including AI chatbots, AI resume generators, and multi-agent systems. I’m looking for practical guidance on best practices, optimization strategies, and ways to improve my overall development workflow. I’d especially appreciate insights on: Reducing inference latency and improving LLM performance Efficient integration of APIs and vector databases (e.g., embeddings, retrieval strategies) Structuring code for scalability, maintainability, and production readiness Tools, libraries, or architectural patterns that have worked well in real projects Any advice, examples, or resources from your experience would be greatly appreciated. |
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Use smaller or distilled models where possible (e.g. fine-tuned smaller LLMs instead of always defaulting to large ones). Enable response streaming to improve perceived latency. Cache frequent prompts and embedding results (Redis works well). Batch requests when generating embeddings. For production, consider model quantization or hosted inference with GPU-backed providers.
Generate embeddings once and store them; never recompute unless data changes. Use hybrid search (vector + keyword filtering) if your vector DB supports it. Keep chunk sizes consistent (usually 300–800 tokens) and include metadata for filtering. Popular stacks that work well: FAISS / Pinecone / Weaviate + LangChain or LlamaIndex Postgres + pgvector for simpler setups
Separate concerns clearly: ingestion → embeddings → retrieval → generation. Keep prompt templates versioned and configurable. Abstract your LLM provider behind a service layer so you can switch models easily. Treat agents as independent services/modules rather than tightly coupled logic. Add basic observability early (logging prompts, latency, token usage).
LangChain / LlamaIndex for orchestration (use selectively, not blindly). FastAPI for clean, scalable backends. OpenTelemetry or simple middleware for tracing. Read production case studies from OpenAI, Anthropic, and Pinecone blogs. General advice Hope this helps — happy to dive deeper into any of these areas. |
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For GenAI projects: start small, measure everything, and avoid over-engineering early. Stream responses, cache repeated prompts/embeddings, batch operations, and keep your LLM calls abstracted behind a service layer. Version prompts, separate agents into independent modules, and use simple observability for latency and token usage. |
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Use smaller or distilled models where possible (e.g. fine-tuned smaller LLMs instead of always defaulting to large ones).
Enable response streaming to improve perceived latency.
Cache frequent prompts and embedding results (Redis works well).
Batch requests when generating embeddings.
For production, consider model quantization or hosted inference with GPU-backed providers.
Generate embeddings once and store them; never recompute unless data changes.
Use hybrid search (vector + keyword filtering) if your vector DB supports it.
Keep chunk sizes consistent (usually 300–800 tokens) and include metadata for filtering.
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