LLM Applications Projects
Build production-ready applications powered by Large Language Models
LLM Applications Projects
Build production-ready applications that leverage LLMs for text generation, structured extraction, content automation, and multi-modal understanding. From streaming chatbots to fine-tuned domain models, each project teaches patterns used in real production systems.
Learning Path
LLM Applications Learning Path
Basic
Intermediate
Advanced
Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Chatbot | Build a conversational AI with streaming responses | ~2 hours |
| Text Summarization | Create a document summarizer with multiple strategies | ~2 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| Structured Extraction | Extract structured data from unstructured text | ~4 hours |
| Content Generation | Build a content generator with templates and styles | ~4 hours |
| Code Assistant | Create an AI coding assistant with context awareness | ~5 hours |
| Prompt Engineering | Master systematic prompting with templates, versioning, and testing | ~5 hours |
| LLM Guardrails & Security | Build security layers with injection defense, PII protection, and moderation | ~6 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| Multi-Modal Application | Build apps that understand text, images, and audio | ~3 days |
| Fine-Tuning LLMs | Fine-tune models for domain-specific tasks | ~4 days |
| LLM Evaluation | Comprehensive testing and evaluation frameworks | ~3 days |
Why Build LLM Applications?
| Benefit | Description |
|---|---|
| Automation | Automate text-heavy workflows and processes |
| Intelligence | Add natural language understanding to any application |
| Scalability | Handle millions of requests with proper architecture |
| Flexibility | Adapt to new use cases through prompting |
Case Studies
Real-world implementations showing LLM applications in production environments.
| Case Study | Industry | Description | Status |
|---|---|---|---|
| Automated Enterprise Reporting | Enterprise | LLM-powered report generation from data | Available |
| Enterprise Customer Service Chatbot | Customer Service | Multi-channel support with intent classification and escalation | Available |
| Internal Knowledge Assistant | Enterprise | Permission-aware RAG for HR/IT helpdesk with Slack integration | Available |
| Content Moderation System | Social Media | AI-powered content moderation with multi-tier classification | Available |
Key Concepts
LLM Applications Key Concepts
Prompting
Streaming
Structured Output
Evaluation
Production
Frequently Asked Questions
Which LLM should I use for my application?
For most applications, start with GPT-4o-mini (fast, cheap, capable) or Claude 3.5 Haiku. Use GPT-4o or Claude 3.5 Sonnet for complex reasoning. For vision tasks, use GPT-4o or Claude 3.5. For cost-sensitive production, consider open-source models like Llama 3.1 or Mistral via Ollama or cloud providers.
How do I reduce LLM API costs in production?
Key strategies: (1) Semantic caching to reuse similar responses (40-60% cost reduction), (2) Use smaller models for simple tasks, route complex queries to larger models, (3) Prompt optimization to reduce token count, (4) Batch similar requests, (5) Set appropriate max_tokens limits. Our MLOps section covers caching in detail.
How do I get structured JSON output from an LLM?
Use JSON mode (OpenAI's response_format: {type: "json_object"}) or function calling. For reliable extraction, use the Instructor library with Pydantic models - it handles retries and validation automatically. Always provide clear schemas and examples in your prompts.
How do I implement streaming responses for better UX?
Use Server-Sent Events (SSE) with FastAPI's StreamingResponse or the stream=True parameter in LLM APIs. Process tokens as they arrive and send them to the frontend. This creates a ChatGPT-like typing effect and reduces perceived latency. Our chatbot project demonstrates this pattern.
How do I protect my LLM application from prompt injection?
Use multiple layers: (1) Input validation and sanitization, (2) Clear separation between system prompts and user input, (3) Output filtering for sensitive content, (4) Rate limiting per user, (5) Content moderation APIs, (6) Monitoring for unusual patterns. Our LLM Guardrails project covers comprehensive security.
When should I fine-tune vs use prompting?
Use prompting first - it's faster to iterate and doesn't require training data. Fine-tune when you need: consistent style/tone across outputs, domain-specific vocabulary, improved performance on specialized tasks, or reduced prompt length (lower costs). Most applications work well with prompting + few-shot examples.
Start with the Chatbot project to learn the fundamentals.