LLM Applications Projects

Build real-world applications using the power of Large Language Models

LLM Applications Projects

Create practical, production-ready applications that leverage LLMs for text generation, analysis, and automation.

Learning Path

┌─────────────────────────────────────────────────────────────────────────────┐
│                       LLM APPLICATIONS LEARNING PATH                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │ BASIC                                                               │    │
│  │   Chatbot ──────────────────► Text Summarization                    │    │
│  └──────────────────────────────────┬──────────────────────────────────┘    │
│                                     │                                       │
│                                     ▼                                       │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │ INTERMEDIATE                                                        │    │
│  │                                                                     │    │
│  │   Structured ──► Content ──► Code ──► Prompt ──► LLM                │    │
│  │   Extraction    Generation  Assistant Engineering Guardrails        │    │
│  │                                                                     │    │
│  └──────────────────────────────────┬──────────────────────────────────┘    │
│                                     │                                       │
│                                     ▼                                       │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │ ADVANCED                                                            │    │
│  │                                                                     │    │
│  │   Multi-Modal App ──► Fine-Tuning LLMs ──► LLM Evaluation           │    │
│  │                                                                     │    │
│  └─────────────────────────────────────────────────────────────────────┘    │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

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                        │
├────────────────────────────────────────────────────────────────────────────┤
│                                                                            │
│                            ┌──────────┐                                    │
│                            │ LLM Apps │                                    │
│                            └────┬─────┘                                    │
│      ┌──────────┬───────────────┼───────────────┬──────────┐               │
│      │          │               │               │          │               │
│      ▼          ▼               ▼               ▼          ▼               │
│  ┌────────┐ ┌─────────┐  ┌───────────┐   ┌──────────┐ ┌──────────┐         │
│  │Prompting││Streaming│  │Structured │   │Evaluation│ │Production│         │
│  └───┬────┘ └────┬────┘  │  Output   │   └────┬─────┘ └────┬─────┘         │
│      │           │       └─────┬─────┘        │            │               │
│  • System     • Server-     • JSON Mode   • Benchmarks  • Caching          │
│    Prompts      Sent Events • Function    • Human       • Rate             │
│  • Few-shot   • Token         Calling       Feedback      Limiting         │
│    Learning     Callbacks   • Pydantic    • Automated   • Cost             │
│  • Chain of   • Real-time     Models        Testing       Management       │
│    Thought      UI                                                         │
│                                                                            │
└────────────────────────────────────────────────────────────────────────────┘

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.

LLM Applications Projects

Build real-world applications using the power of Large Language Models