Chapter 3
Tools Required for a GenAI Project
Learning Objective
Learn the major tools used to build, test, deploy, and monitor enterprise GenAI applications.
What it means
A GenAI project needs more than an LLM API. Teams usually need tools for development, orchestration, retrieval, APIs, security, testing, deployment, and monitoring. Tool selection should be based on use case complexity, data sensitivity, team skills, cloud preference, and cost constraints.
Healthcare Example
A healthcare document assistant may use FastAPI for the service, LangGraph for workflow orchestration, Azure OpenAI for model calls, Azure AI Search for vector retrieval, GitHub for code control, Docker for packaging, Kubernetes for deployment, and monitoring dashboards for production support.
Architecture Flow
Tool Categories
| Category | Examples | Used for |
|---|---|---|
| LLM Providers | OpenAI, Azure OpenAI, Anthropic, Gemini, Llama | Text generation, reasoning, extraction, summarization |
| Frameworks | LangChain, LangGraph, LlamaIndex | Prompt workflows, RAG, agents, stateful orchestration |
| Vector Databases | Pinecone, Weaviate, Chroma, FAISS, Azure AI Search | Semantic search and RAG retrieval |
| API Frameworks | FastAPI, Flask | Expose GenAI functionality as services |
| DevOps | GitHub, GitHub Actions, Azure DevOps, Jenkins | Version control and CI/CD |
| Containers | Docker, Kubernetes | Packaging, scaling, deployment |
| Observability | Prometheus, Grafana, OpenTelemetry, cloud logs | Monitoring cost, latency, errors, usage |
Common Mistakes
- Selecting tools only because they are popular.
- Using an agent framework when a simple API would work.
- Ignoring security and audit requirements during tool selection.
- Choosing a vector database without evaluating retrieval quality.
Interview Q&A
Q: What tools would you use in a GenAI project?
A: I would select tools across model access, RAG, orchestration, APIs, DevOps, containers, monitoring, and security. The exact selection depends on data sensitivity, scale, latency, and cloud standards.
Q: Do all projects need LangGraph?
A: No. LangGraph is valuable for stateful, branching, multi-agent workflows. Simple summarization or Q&A can often be built with a lighter chain or direct API.
Architect Takeaway
Tools should support the architecture. They should not define it. Start with requirements, then choose tools.