Advanced AI Applications
Build and integrate custom AI solutions using fine-tuning, APIs, embeddings, and retrieval
What You'll Learn
- ✓How fine-tuning differs from prompt engineering and when it's appropriate
- ✓How to call AI models via APIs (OpenAI, Anthropic, Google) and handle rate limits, latency, and error handling
- ✓What embeddings are, how to generate them, and how to use them in vector databases
- ✓Introduction to vector search, hybrid retrieval, and RAG architecture
- ✓Building simple AI-powered tools (chatbots, summarizers, code assistants) with function calls or plugin architectures
- ✓Challenges of deploying AI at scale: cost, latency, reliability, security, and compliance
Key Ideas
Fine-tuning involves training a pre-trained model on a smaller, domain-specific dataset. Unlike prompt engineering (which manipulates inputs), fine-tuning changes the model's weights, requiring labelled examples, computational resources, and careful regularization to avoid catastrophic forgetting. Use fine-tuning when your prompts cannot capture complex domain-specific patterns or when you need consistent behavior across tasks.
Examples:
- • Use case: Customer support chatbot with company-specific knowledge
- • Use case: Legal document analysis with domain terminology
- • When to use: Complex patterns that prompts can't capture consistently
AI providers expose REST APIs that allow you to send prompts and receive outputs programmatically. Manage authentication tokens securely and implement retries. Use streaming endpoints for faster responses on long outputs.
Examples:
- • Synchronous: Direct API calls for chatbots and summarization
- • Asynchronous: Queue systems for long-running translation tasks
- • Optimization: Caching to reduce costs and latency
An embedding is a dense numerical representation of text, code, images, or other data. Embeddings capture semantic similarity; vectors close together represent similar meanings. Vector databases (Pinecone, Weaviate, FAISS) store embeddings and enable nearest-neighbor search, which is essential for retrieval tasks and RAG.
Examples:
- • Generate embeddings using models like text-embedding-ada-002
- • Distance metrics: cosine similarity, Euclidean distance
- • Use cases: Semantic search, document similarity, recommendation systems
Retrieval-Augmented Generation enhances model outputs by incorporating external knowledge. It has four components: ingestion (load authoritative data), retrieval (find relevant chunks), augmentation (combine context with the query), and generation. RAG provides real-time data access, trust through citations, control over sources, and cost-effectiveness compared with fine-tuning.
Examples:
- • Ingestion: Load and chunk authoritative documents
- • Retrieval: Find relevant passages using vector search
- • Augmentation: Combine retrieved context with user query
- • Generation: Produce answer with citations
Dive Deeper
Explore the mechanism, mastery techniques, and critical thinking considerations. Click to expand each layer.
Suggested Resources
OpenAI
LangChain
Try This Now
Put your learning into practice with these hands-on exercises. Copy the prompts and try them in your favorite AI tool.
Design a fine-tuning experiment: (1) Define the domain and task, (2) Describe the dataset needed (size, format, labels), (3) Choose a base model, (4) Outline evaluation metrics, (5) Compare to prompt engineering baseline.
Plan a vector search implementation: (1) Choose 10-20 documents from your field, (2) Select an embedding model, (3) Design the indexing strategy, (4) Define test queries, (5) Outline how you'll measure search quality.
Design a RAG system: (1) Select a knowledge domain and corpus, (2) Choose chunking strategy and chunk size, (3) Select retrieval algorithm, (4) Design the prompt template for generation, (5) Define success metrics (accuracy, citation quality, latency).
Reflection Questions
- 1.When should you invest in fine-tuning rather than building a retrieval system?
- 2.How does embedding choice affect retrieval quality? What trade-offs exist between small and large embedding models?
- 3.How can you design AI tools that are secure, trustworthy, and maintainable?