AI for Developers — APIs, LLMs & Application Architecture
Target audience: Software engineers, full-stack developers, and technical leads.
Applicable duration: 60 mins · 120 mins
Integrating artificial intelligence into software applications involves far more than simply firing an HTTP request to an API endpoint. Production software demands deterministic output formatting, defensive error handling against rate limits and downtime, optimized latency budgets, semantic vector search, and disciplined state management for autonomous workflows.
In this technical 1:1 live session designed specifically for software engineers, you will pair with a senior AI engineer to build real-world AI applications. You will work directly in code (Python or TypeScript), integrating commercial AI APIs, mastering large language model fundamentals, and implementing state-of-the-art design patterns.
Your mentor will guide you through working with dense mathematical embeddings, querying high-performance vector databases, constructing robust Retrieval-Augmented Generation (RAG) pipelines, and wiring autonomous tool-calling agents. Finally, you will explore best practices for deploying AI applications into cloud production environments.
What you will learn
- AI APIs Integration Interfacing programmatically with OpenAI, Anthropic Claude, and Google Gemini SDKs with asynchronous requests and retries.
- Building AI Applications Designing client-server architectures, streaming real-time tokens to UI frontends via Server-Sent Events (SSE).
- LLM Fundamentals Deep-diving into tokenization, context window budget management, temperature calibration, and probabilistic inference mechanics.
- Embeddings and Vector Databases Generating mathematical text embeddings and indexing high-dimensional vectors in ChromaDB, Pinecone, or PostgreSQL pgvector.
- Retrieval-Augmented Generation (RAG) Engineering end-to-end retrieval pipelines combining document chunking, semantic similarity search, and factual context injection.
- AI Agents Implementing autonomous agentic loops with function-calling, state machines, and human-in-the-loop approval gates using LangGraph.
- Deploying AI Applications Packaging AI microservices using Docker, managing API key security, and establishing production token observability.
Qualifications you will acquire
- Full-Stack AI Application Engineering Competency: Proven capability to design, code, and deploy custom software applications powered by generative AI.
- Production API Integration & Resilience Mastery: Demonstrated skill in implementing retry logic, rate limit backoff, and multi-model failover routing.
- Vector Search & Embedding Architecture Capability: Competence in indexing unstructured enterprise data and querying vector spaces via cosine similarity.
- Enterprise AI Deployment Qualification: Ability to containerize, secure, and monitor production AI microservices.
Key features
- 100% 1:1 Code Pair-Programming: Build and debug real software applications live alongside an active AI software engineer.
- Direct Tech Stack Alignment: Choose between Python (FastAPI/LangChain) or TypeScript (Node.js/Next.js/Vercel AI SDK).
- Live Architecture Review: Receive real-time refactoring feedback and security audits on your existing code repositories.
- Turnkey Production Boilerplate: Walk away with clean, production-ready starter repositories featuring streaming responses and API connectors.
- Post-Session Technical Roadmap: Receive a tailored architectural blueprint and documentation guide for scaling your application.
