- 1-2Days
- IntermediateLevel
- 4Major Projects
Course Overview
Learn to design, build, deploy, and scale AI applications from concept to production. This comprehensive course covers the entire AI product lifecycle, from strategic planning and feature design to deployment, monitoring, and continuous improvement. Build production-ready applications with hands-on projects using real-world technologies.
Product managers, full-stack developers, AI engineers, startup founders, and technical leaders who want to ship production-ready AI applications.
Basic programming experience (e.g., Python), understanding of web applications and APIs, familiarity with databases. Some exposure to AI concepts helpful but not required.
Comprehensive Curriculum
14 topics covering the complete AI product development lifecycle
- 01
AI Product Strategy and Feature Design
Learn to identify high-impact AI features, validate product ideas, and design user experiences that use AI well. Understand product-market fit for AI applications.
- 02
Model Selection and Evaluation for Production
Compare different AI models and learn to select the right one for your use case. Understand trade-offs between performance, cost, latency, and accuracy.
- 03
Prompt Engineering Patterns and Best Practices
Master advanced prompt engineering techniques including few-shot learning, chain-of-thought prompting, and prompt optimization strategies for consistent results.
- 04
RAG (Retrieval-Augmented Generation) Systems
Build sophisticated RAG systems that combine retrieval and generation. Learn chunking strategies, embedding models, retrieval algorithms, and response synthesis.
- 05
Vector Databases and Semantic Search
Implement semantic search with vector databases like Pinecone, Weaviate, and Qdrant. Understand embeddings, similarity search, and hybrid search approaches.
- 06
API Design and Integration Strategies
Design APIs for AI applications that hold under load. Learn about streaming responses, rate limiting, error handling, and integrating with external AI services.
- 07
Fine-tuning vs Prompt Engineering Trade-offs
Understand when to use prompt engineering versus fine-tuning. Learn cost-benefit analysis, implementation approaches, and maintenance considerations.
- 08
Deployment Architectures and Infrastructure
Explore deployment options including serverless, containerization, and managed services. Learn to architect scalable, reliable AI systems.
- 09
Monitoring, Logging, and Observability
Implement comprehensive monitoring for AI applications. Track model performance, costs, latency, errors, and user satisfaction metrics.
- 10
A/B Testing and Experimentation
Design and run experiments to improve AI features. Learn statistical significance, variant testing, and data-driven decision making.
- 11
Cost Optimization and Scaling
Optimize AI application costs through caching, batching, model selection, and efficient architectures. Learn to scale from prototype to production.
- 12
Security, Privacy, and Compliance
Implement security best practices for AI applications. Handle sensitive data, ensure privacy compliance (GDPR, etc.), and prevent prompt injection attacks.
- 13
Ethics and Responsible AI Deployment
Build ethical AI products. Address bias, fairness, transparency, and accountability in production systems.
- 14
User Feedback Loops and Iteration
Create systems to collect user feedback, analyze usage patterns, and continuously improve AI features based on real-world data.
What you'll achieve
By the end of this course, you will be able to:
- Design AI features that solve a real user problem
- Build and deploy complete AI applications from scratch to production
- Implement production-grade RAG systems with proper chunking and retrieval
- Build semantic search applications using vector databases
- Optimize AI applications for cost, performance, and scale
- Monitor and continuously improve AI systems in production
- Handle security, privacy, and compliance requirements confidently
- Ship AI products that users love and that grow your business
Build portfolio-worthy AI applications through hands-on projects
- 01Project 1: RAG-Powered Q&A SystemBuild a complete question-answering system using RAG. Implement document ingestion, chunking, embedding, retrieval, and response generation. Deploy to production.
- 02Project 2: Semantic Search ApplicationCreate a semantic search engine with vector databases. Implement hybrid search combining keyword and semantic search for optimal results.
- 03Project 3: a chatbot that remembersBuild an intelligent chatbot with conversation memory, tool use capabilities, and personalization. Handle multi-turn conversations effectively.
- 04Final Project: Complete AI ApplicationDesign and build a full-stack AI application from ideation to deployment. Include monitoring, cost optimization, and user feedback loops. Present your product.
What people ask before they enrol.
Both, and deliberately. The hard part of an AI product is the decisions at the seam: what the model is allowed to do, what a person checks, what happens when it is wrong. Those decisions need the two roles in the same room.
Yes. The course runs through the lifecycle on a working build, including the parts that are usually skipped: evaluation, cost, and what to do when quality drifts after launch.
Enough to be dangerous is enough to start. The course assumes you will use existing models rather than train your own, which is what almost every AI product does.
This one is about shipping a product people use. That one is about building the tools your engineers use. Different audiences, different failure modes.
Bring us the problem nobody has cracked yet.
We are a small team of senior specialists. We pick the right model and the right layer, and we build the least machinery that does the job. You get a call with an engineer, not a sales deck.