Lilly Tech Systems
Building Multi-Model AI Apps
Learn to combine LLMs, embeddings, vision, speech, and other specialized AI models to build powerful real-world applications. From RAG pipelines to production orchestration — master the art of composing multiple models into cohesive systems.
Why Multi-Model Architecture?
No single AI model can do everything well. The most powerful AI applications combine multiple specialized models working together — each contributing its unique strength.
LLMs for Reasoning
Large Language Models like Claude, GPT-4, and Gemini provide natural language understanding, reasoning, and generation capabilities.
Embeddings for Search
Embedding models convert text into dense vectors, enabling semantic search, similarity matching, and retrieval-augmented generation.
Vision for Understanding
Vision models analyze images, documents, diagrams, and video frames — extracting structured information for downstream processing.
Speech for Interaction
Speech-to-text and text-to-speech models power voice interfaces, transcription, and real-time conversational AI experiences.
Your Learning Path
Follow these 13 lessons to go from fundamentals to production-ready multi-model applications.
1. Introduction
Why multi-model architecture matters. The shift from single-model apps to composed AI systems. Key patterns and the modern AI stack.
2. RAG Applications
Retrieval-Augmented Generation combining embedding models and LLMs. Chunking, vector stores, and advanced retrieval strategies.
3. Document Processing
OCR + Vision + LLM pipelines for intelligent document extraction, classification, and summarization at scale.
4. Conversational AI
Speech-to-text + LLM + text-to-speech for voice assistants. Dialog management and real-time streaming architecture.
5. Content Creation Pipeline
LLM + image generation + layout models for automated content creation. Blog posts, social media, and marketing at scale.
6. Vision + LLM Apps
Combining vision models with LLMs for image understanding, visual Q&A, diagram interpretation, and multimodal reasoning.
7. Translation & Multilingual
Translation models + LLMs + speech for multilingual AI apps. Localization pipelines and cross-language retrieval.
8. Recommendation Systems
Embedding models + collaborative filtering + LLMs for intelligent recommendation engines with natural language explanations.
9. Orchestration Frameworks
LangChain, LlamaIndex, Haystack, and Semantic Kernel. How to wire models together with production-grade frameworks.
10. Model Serving
Serving multiple models efficiently. API gateways, model routers, batching, caching, and GPU resource management.
11. Vector Databases
Deep dive into vector storage. Indexing algorithms, hybrid search, metadata filtering, and scaling to billions of vectors.
12. Production Pipelines
End-to-end production architecture. Error handling, monitoring, A/B testing, rollback strategies, and CI/CD for AI pipelines.
13. Best Practices
Cost optimization, latency budgets, reliability patterns, testing strategies, and security for multi-model systems.
What You'll Build
Throughout this course, you'll learn to create these real-world multi-model applications:
Enterprise Knowledge Base
A RAG-powered application that ingests company documents, chunks and embeds them into a vector database, and lets employees ask natural language questions. Combines embedding models for semantic search with an LLM for answer generation, plus a reranking model for precision. Handles PDFs, Slack messages, Confluence pages, and code repositories.
Intelligent Document Processor
A pipeline that automatically processes invoices, contracts, and receipts. Uses a vision model to extract text from scanned documents, an LLM to understand structure and extract key fields, an embedding model to classify document types, and a code generation model to create structured JSON output. Processes thousands of documents per hour.
Voice-Powered AI Assistant
A conversational AI system that listens, understands, and responds in natural speech. Chains speech-to-text (Whisper) for transcription, an LLM for understanding and response generation, a retrieval model for grounding answers in documentation, and text-to-speech for natural voice output. Supports real-time streaming for low-latency conversations.
Content Creation Engine
An automated content pipeline for marketing teams. Uses an LLM to generate blog posts and social media copy, an image generation model (DALL-E, Stable Diffusion) for visuals, a translation model for localization into 10+ languages, and a sentiment model for tone analysis. Produces consistent, on-brand content at scale with human review checkpoints.
Prerequisites
This course assumes intermediate developer knowledge. Here's what you should be comfortable with before starting:
Python Proficiency
You should be comfortable writing Python, using pip/conda for package management, and working with async/await patterns.
Basic AI/ML Concepts
Understanding of what LLMs are, how APIs work (REST, JSON), and basic concepts like tokens, prompts, and embeddings.
API Experience
Experience calling APIs (OpenAI, Anthropic, or similar). Familiarity with API keys, rate limits, and error handling.
Docker Basics
Basic Docker knowledge helps for the production lessons, but isn't strictly required for the fundamentals.
The Multi-Model Landscape
Understanding the ecosystem of model types you'll be composing:
| Model Type | Purpose | Examples | Lesson |
|---|---|---|---|
| LLM (Text Generation) | Reasoning, generation, summarization | Claude, GPT-4, Gemini, Llama 3 | All lessons |
| Embedding | Semantic search, similarity | OpenAI Ada, Cohere Embed, BGE | Lessons 2, 8, 11 |
| Vision | Image/document understanding | Claude Vision, GPT-4V, LLaVA | Lessons 3, 6 |
| Speech-to-Text | Audio transcription | Whisper, Deepgram, AssemblyAI | Lesson 4 |
| Text-to-Speech | Voice synthesis | ElevenLabs, OpenAI TTS, Azure | Lesson 4 |
| Image Generation | Creating images from text | DALL-E 3, Stable Diffusion, Midjourney | Lesson 5 |
| Reranking | Relevance scoring | Cohere Rerank, ColBERT, BGE Reranker | Lessons 2, 8 |
| Translation | Language conversion | NLLB, MarianMT, DeepL | Lesson 7 |
| Classification | Categorization, sentiment | BERT, DeBERTa, SetFit | Lessons 3, 8 |
| Code Generation | Code writing, completion | Claude Code, Codex, StarCoder | Lesson 12 |