“Help Needed: Tips and Best Practices for My GenAI Projects”

[P-r-e-m-i-u-m]

Body

Hi everyone,

I’m currently building projects in Generative AI, including AI chatbots, AI resume generators, and multi-agent systems. I’m looking for guidance on best practices, optimization strategies, and tips to improve my project workflow.

Specifically, I’d love advice on:

Reducing inference latency for LLMs

Efficiently integrating APIs and Vector Databases

Improving code structure and project scalability

Any resources, tools, or techniques that have worked for you

Any feedback, suggestions, or examples from your experience would be highly appreciated!

Thank you in advance for your help.

Guidelines

• I have read and understood this category's guidelines before making this post.

[healer0805]

Great!
Here is what I wanna break down.

Once these projects move past the demo phase, a few things start to matter a lot more.

Latency:
Most of the time it’s not the model; it’s how often you call it. Cache hard, stream responses, and don’t ask the LLM to do work your code can handle. Fewer calls beats a smaller model almost every time.

APIs + vector DBs:
Treat retrieval as a first-class system. Filter early, keep embeddings stable, and don’t mix heavy writes with hot reads in the same index. That’s where things quietly slow down.

Structure & scaling:
If every part of the app talks to the LLM directly, it gets messy fast. A thin orchestration layer that owns prompts, retries, and fallbacks keeps the rest of the code clean and easy to scale.

Tools & habits:
Simple metrics go a long way. Track call counts, latency, and cache hits. Optimize what users actually trigger, not what "looks" expensive in theory.

This is just my opinion.

[ramcharan032785-code]

Hi! Your projects sound really exciting. For best practices:

Reducing inference latency: Consider using model quantization, caching repeated responses, and optimizing batch sizes. Tools like ONNX Runtime or TensorRT can also help.

Integrating APIs & Vector Databases: Use async calls, standardize your client code, and pre-compute embeddings when possible. For vector DBs like Pinecone or Milvus, proper indexing and efficient similarity search tuning are key.

Improving code structure & scalability: Keep components modular, follow clean architecture principles, and use containerization (Docker) with CI/CD pipelines for consistent deployment.

For structured learning and resources on Generative AI workflows and best practices, you can check: https://www.icertglobal.com/