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

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

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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.

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[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/

[RohitWaghire]

Alright, so you're building some solid GenAI projects.

On latency—this is where people get stuck the most. First thing: are you streaming responses? If you're not, start there. Users perceive streamed output as way faster even when total time is similar. It's just psychology, but it works.
Then look at your model choice. I know everyone wants to throw GPT-4 at everything, but honestly, for a lot of chatbot interactions, something like GPT-3.5 or even Claude Haiku handles it fine and cuts your latency in half. Save the big models for when you actually need the reasoning power. Same goes for resume generation—most of that is template filling and light reformatting. You don't need a cannon to kill a mosquito. Caching is your friend. If you're making repeated calls with similar context, implement prompt caching. With Anthropic's API, for instance, you can cache system prompts and common context chunks. Cuts costs and speeds things up considerably.

For the API and vector database integration—okay, this is where projects get messy fast. Keep your database queries separate from your LLM calls. I mean really separate. Don't inline everything. When I see someone's code with database calls nested inside LLM response handlers, it's a nightmare to debug and optimize. Use connection pooling for your vector DB. Whether you're using Pinecone, Weaviate, or Qdrant, don't open a new connection for every query. And batch your embedding operations—if you're embedding user inputs one at a time, you're leaving performance on the table. Actually, here's something people miss: precompute what you can. For a resume generator, you probably have standard sections and common phrasing. Embed those once, store them, reuse them. Don't regenerate embeddings for the same content.

Code structure—this matters more than people think. I'd suggest: Separate your prompts from your code. Put them in config files or a dedicated prompts module. You'll thank yourself when you're iterating on prompt design and don't have to hunt through Python files. Abstract your LLM calls behind a service layer. Makes it trivial to swap models, add retry logic, or implement fallbacks. For multi-agent systems especially, you want each agent to be its own module with clear interfaces. For multi-agent stuff specifically, think hard about your orchestration pattern. Are your agents working sequentially, in parallel, or some mix? Use async/await properly—don't make one agent wait for another if they're doing independent work. I've seen projects cut execution time by 60% just by properly parallelizing agent tasks.

Real workflow stuff that helps:
Use something like LangSmith or Weights & Biases for tracking your LLM calls. When things go wrong—and they will—you need to see what prompt actually went out, what came back, and how long it took. Debugging blind is miserable. Version your prompts. Seriously. Git works fine for this. When you change a prompt and everything breaks, you want to know exactly what changed. For testing, build a small eval set early. Ten to twenty representative examples with expected outputs. Run them regularly. Saves you from accidentally making things worse while trying to improve them. Don't over-engineer early. I see a lot of projects that try to build perfect abstractions from day one and never ship. Get something working, find where it actually hurts, then fix that. Premature optimization and all that.

Resources that are actually useful:
The Anthropic cookbook has practical patterns that aren't just toy examples. Same with OpenAI's guides—skip the hype, focus on the implementation patterns. For vector databases, each one's documentation is actually pretty good. Pinecone's guides on chunking strategies are solid regardless of which DB you use. Simon Willison's blog is genuinely helpful for practical LLM application stuff. He builds real things and writes about what actually works. One last thing—for multi-agent systems, start simple. Two agents, clear handoffs, observable behavior. Then add complexity. I've seen too many projects try to build five-agent systems right away and end up with an unmaintainable mess where nobody knows why the agents are doing what they're doing. The unsexy truth is that most improvement comes from profiling, measuring, and fixing bottlenecks methodically. Not from clever architectures or fancy techniques. Build instrumentation early so you can actually see where time and money are going.

[rinas21]

For GenAI projects: Stream responses and cache repeated prompts to reduce latency. Keep API calls and vector DB queries separate, precompute embeddings where possible, and batch operations. Structure your code with a thin service layer for LLM calls, modular agents, and versioned prompts. Use simple metrics to track performance and profile bottlenecks before optimizing

[mergisi]

Great questions! Here are some practical tips from building GenAI applications:

Latency Reduction:

• Stream responses wherever possible - users perceive faster responses even if total time is similar
• • Cache frequently used embeddings and API responses
• • Consider smaller, specialized models for specific tasks rather than one large model for everything
    Vector DB Integration:
• Start with managed solutions (Pinecone, Weaviate Cloud) before self-hosting - infrastructure overhead is real
• • Proper chunking strategies often matter more than which vector DB you choose
• • Test retrieval quality with real user queries before building complex RAG pipelines
    Code Structure:
• Separate your LLM service layer from business logic - makes testing and model swapping much easier
• • Version your prompts alongside your code
• • Build evaluation harnesses early
    Useful Resources:
• Simon Willison's blog for practical LLM patterns
• • Anthropic Cookbook for implementation examples
• • For database-related AI tasks like text-to-SQL, ai2sql.io has been useful for quickly prototyping queries before implementing them in applications
    Good luck with your projects!

[thisistanishq]

Simple tip that helped my GenAI projects a lot:
Most latency issues come from running everything sequentially.

What worked for me:

*Cache repeated prompts
*Run DB calls, embeddings, and tools in parallel (async)
*Use smaller models when possible
*Stream responses instead of waiting for full output
*This alone made apps feel much faster without changing models.