3 days Software engineers, data engineers
AI for Developers
Lightbridge Labs AI for Developers is a three-day technical training that teaches software engineers to build and deploy AI-powered applications. Curriculum covers ML fundamentals, LLM API integration, RAG architecture, fine-tuning workflows, evaluation methods, and production deployment patterns. Every concept is reinforced through hands-on exercises using real models and live environments.
Three-day curriculum
Day 1
ML Fundamentals and LLM Integration
- How ML models work: supervised learning, embeddings, and neural networks in plain terms
- LLM architecture: tokens, attention, context windows, and inference
- API integration patterns: streaming, batching, retry logic, and error handling
- Prompt engineering for developers: structured outputs, function calling, and tool use
Day 2
Embeddings, RAG, and Vector Search
- Text embeddings: how they work, model selection, and dimensionality trade-offs
- Vector databases: Pinecone, Weaviate, pgvector. Architecture and query patterns
- RAG architecture: document ingestion, chunking strategies, retrieval optimization
- Building a production RAG system: hands-on end-to-end exercise
Day 3
Fine-Tuning, Evaluation, and Production
- Fine-tuning: when to fine-tune vs. RAG vs. prompt engineering. Dataset preparation and training
- Evaluation frameworks: automated testing, human evaluation, benchmark design
- Production deployment: monitoring, cost management, latency optimization, A/B testing
- Capstone project: build and deploy an AI-powered feature end-to-end
Engineers teaching engineers.
Lightbridge Labs instructors have shipped production ML systems across enterprise environments.
ISO 27001
Certification in progress
SOC 2 Type II
Certification in progress
ISO 42001
Certification in progress
Common questions
- What prerequisites are required for the Lightbridge Labs AI for Developers program?
- Participants should have at least 2 years of software engineering experience and be proficient in Python. Familiarity with REST APIs, SQL, and basic statistics is helpful but not required. No prior ML experience is needed. We cover fundamentals before advancing to production patterns.
- What will developers be able to build after this program?
- After completing the program, developers can build ML-powered features, integrate large language models into applications, design RAG systems, implement embedding pipelines, fine-tune models for specific tasks, set up evaluation frameworks, and deploy ML models with proper monitoring and error handling.
- How is this different from an online ML course?
- This program focuses on production software engineering with AI, not data science theory. Every module covers what developers actually encounter: API integration patterns, error handling, cost management, latency optimization, testing strategies, and deployment workflows. The exercises use real APIs and production-grade architectures.
- What programming languages and tools are used in the exercises?
- The primary language is Python. Exercises use popular AI libraries including OpenAI SDK, Anthropic SDK, LangChain, Hugging Face Transformers, and vector databases (Pinecone, Weaviate, pgvector). Each participant receives a cloud development environment pre-configured with all tools.
- Can this training be customized for our technology stack?
- Yes. Lightbridge Labs customizes the AI for Developers program to align with your organization’s technology stack, coding standards, and use cases. We can adjust exercises to use your preferred languages, frameworks, and cloud providers. Contact us to discuss customization options.
- Is there support after the training ends?
- Participants receive 30-day post-program access to the exercise environment, a comprehensive code repository with all examples and patterns, and access to the Lightbridge Labs developer reference library. Organizations can also engage Lightbridge Labs for ongoing mentorship and code review.
Make your engineering team AI-capable.
Contact us to schedule AI for Developers for your engineering team. On-site, virtual, or hybrid.