LLM Capstone Project

This capstone project challenges you to build a complete LLM application that solves a real-world problem. You'll go through the full software development lifecycle, from requirements gathering to production deployment.

Project Selection

Choosing a Project

Select a project that:

1. Solves a real problem: Addresses a genuine need
2. Is achievable: Can be completed in the time frame
3. Demonstrates skills: Showcases LLM knowledge
4. Has data available: Has accessible training/evaluation data
5. Can be deployed: Can run in a production environment

Project Ideas

Project Design Phase

Requirements Gathering

Requirements template:

## Functional Requirements
- [ ] Core feature 1: Description
- [ ] Core feature 2: Description
- [ ] User interaction model: Chat/API/Batch
- [ ] Output format: Text/JSON/Structured

## Non-Functional Requirements
- [ ] Latency: < X seconds
- [ ] Throughput: X requests/minute
- [ ] Accuracy: X% on evaluation set
- [ ] Availability: X% uptime

## Constraints
- [ ] Budget: $X
- [ ] Timeline: X weeks
- [ ] Team size: X people
- [ ] Technology stack: Specific tools/frameworks

## Ethical Considerations
- [ ] Bias mitigation strategies
- [ ] Privacy requirements
- [ ] Safety measures
- [ ] Transparency needs

Architecture Design

Typical architecture components:

┌─────────────────────────────────────────────────────────────┐
│                     User Interface                          │
├─────────────────────────────────────────────────────────────┤
│                     API Gateway                             │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │   Prompt     │  │   Retrieval │  │   Post-     │         │
│  │   Engine     │  │   System    │  │   Processor │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
├─────────────────────────────────────────────────────────────┤
│                     LLM Engine                              │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │   Model      │  │   Cache     │  │   Monitor   │         │
│  │   Server     │  │   System    │  │   System    │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
└─────────────────────────────────────────────────────────────┘

Technology Selection

Criteria:

1. Performance: Latency, throughput
2. Scalability: Ability to handle growth
3. Cost: Infrastructure and operational costs
4. Community: Support and ecosystem
5. Team expertise: Existing knowledge

Implementation Phase

Project Structure

llm-capstone/
├── src/
│   ├── __init__.py
│   ├── config.py
│   ├── models/
│   │   ├── __init__.py
│   │   └── llm.py
│   ├── services/
│   │   ├── __init__.py
│   │   ├── retrieval.py
│   │   └── generation.py
│   ├── api/
│   │   ├── __init__.py
│   │   └── endpoints.py
│   └── utils/
│       ├── __init__.py
│       └── helpers.py
├── tests/
│   ├── unit/
│   ├── integration/
│   └── evaluation/
├── data/
│   ├── raw/
│   ├── processed/
│   └── evaluation/
├── notebooks/
├── docs/
├── docker/
├── requirements.txt
├── Dockerfile
└── README.md

Configuration Management

from pydantic import BaseModel
from typing import Optional
from enum import Enum

class Environment(str, Enum):
    DEVELOPMENT = "development"
    STAGING = "staging"
    PRODUCTION = "production"

class LLMConfig(BaseModel):
    model_name: str = "meta-llama/Llama-3-8B-Instruct"
    temperature: float = 0.7
    max_tokens: int = 512
    top_p: float = 0.9
    
class RAGConfig(BaseModel):
    chunk_size: int = 1000
    chunk_overlap: int = 200
    retrieval_top_k: int = 5
    
class AppConfig(BaseModel):
    environment: Environment = Environment.DEVELOPMENT
    llm: LLMConfig = LLMConfig()
    rag: RAGConfig = RAGConfig()
    debug: bool = False

Model Implementation

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from typing import List, Dict, Any

class LLMEngine:
    def __init__(self, config: LLMConfig):
        self.config = config
        self.tokenizer = AutoTokenizer.from_pretrained(config.model_name)
        self.model = AutoModelForCausalLM.from_pretrained(
            config.model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )
    
    def generate(self, prompt: str, **kwargs) -> str:
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
        
        generation_kwargs = {
            "max_new_tokens": self.config.max_tokens,
            "temperature": self.config.temperature,
            "top_p": self.config.top_p,
            "do_sample": self.config.temperature > 0
        }
        generation_kwargs.update(kwargs)
        
        outputs = self.model.generate(**inputs, **generation_kwargs)
        response = self.tokenizer.decode(
            outputs[0][inputs.shape[-1]:], 
            skip_special_tokens=True
        )
        return response
    
    def generate_with_history(
        self, 
        messages: List[Dict[str, str]], 
        **kwargs
    ) -> str:
        prompt = self.tokenizer.apply_chat_template(
            messages, 
            tokenize=False, 
            add_generation_prompt=True
        )
        return self.generate(prompt, **kwargs)

Retrieval System

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from typing import List, Dict, Any

class RetrievalSystem:
    def __init__(self, config: RAGConfig):
        self.config = config
        self.embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2"
        )
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=config.chunk_size,
            chunk_overlap=config.chunk_overlap
        )
        self.vectorstore = None
    
    def index_documents(self, documents: List[Dict[str, Any]]):
        texts = [doc["content"] for doc in documents]
        metadatas = [doc["metadata"] for doc in documents]
        
        splits = self.text_splitter.create_documents(texts, metadatas)
        self.vectorstore = Chroma.from_documents(
            splits, 
            self.embeddings
        )
    
    def retrieve(self, query: str) -> List[Dict[str, Any]]:
        if self.vectorstore is None:
            return []
        
        results = self.vectorstore.similarity_search_with_score(
            query, 
            k=self.config.retrieval_top_k
        )
        
        return [
            {
                "content": doc.page_content,
                "metadata": doc.metadata,
                "score": score
            }
            for doc, score in results
        ]

API Implementation

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import uvicorn

app = FastAPI(title="LLM Application API")

class GenerateRequest(BaseModel):
    prompt: str
    max_tokens: Optional[int] = 512
    temperature: Optional[float] = 0.7

class GenerateResponse(BaseModel):
    text: str
    tokens_used: int
    latency_ms: float

@app.post("/generate", response_model=GenerateResponse)
async def generate_text(request: GenerateRequest):
    try:
        start_time = time.time()
        
        # Generate response
        response = llm_engine.generate(
            request.prompt,
            max_new_tokens=request.max_tokens,
            temperature=request.temperature
        )
        
        latency = (time.time() - start_time) * 1000
        
        return GenerateResponse(
            text=response,
            tokens_used=len(tokenizer.encode(response)),
            latency_ms=latency
        )
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/rag/generate")
async def rag_generate(request: GenerateRequest):
    # Retrieve relevant documents
    documents = retrieval_system.retrieve(request.prompt)
    
    # Construct prompt with context
    context = "\n\n".join([doc["content"] for doc in documents])
    prompt = f"""Context: {context}

Question: {request.prompt}

Answer:"""
    
    # Generate response
    response = llm_engine.generate(prompt)
    
    return {
        "answer": response,
        "sources": documents
    }

Evaluation Phase

Evaluation Framework

Evaluation dimensions:

1. Functional: Does it do what it should?
2. Performance: Does it meet latency/throughput requirements?
3. Quality: Are outputs accurate and useful?
4. Usability: Is it easy to use?
5. Robustness: Does it handle edge cases?

Evaluation Metrics

class ProjectEvaluator:
    def __init__(self, test_cases: List[Dict]):
        self.test_cases = test_cases
        self.results = []
    
    def evaluate_functional(self, system):
        correct = 0
        for test_case in self.test_cases:
            output = system.process(test_case["input"])
            if self.check_output(output, test_case["expected"]):
                correct += 1
        
        return correct / len(self.test_cases)
    
    def evaluate_performance(self, system, num_requests=100):
        latencies = []
        for _ in range(num_requests):
            start = time.time()
            system.process("Test input")
            latencies.append(time.time() - start)
        
        return {
            "avg_latency": sum(latencies) / len(latencies),
            "p95_latency": sorted(latencies)[int(0.95 * len(latencies))],
            "p99_latency": sorted(latencies)[int(0.99 * len(latencies))]
        }
    
    def evaluate_quality(self, system, evaluator_llm):
        scores = []
        for test_case in self.test_cases:
            output = system.process(test_case["input"])
            score = evaluator_llm.evaluate(
                test_case["input"],
                output,
                test_case.get("criteria", [])
            )
            scores.append(score)
        
        return sum(scores) / len(scores)

User Testing

User testing protocol:

1. Recruit users: Find representative users
2. Define tasks: Create realistic usage scenarios
3. Observe usage: Watch how users interact
4. Gather feedback: Collect qualitative and quantitative feedback
5. Iterate: Improve based on findings

Deployment Phase

Deployment Architecture

Deployment options:

1. Cloud: AWS, GCP, Azure
2. On-premises: Self-hosted infrastructure
3. Edge: Local deployment
4. Hybrid: Combination of cloud and edge

Containerization

# Dockerfile
FROM python:3.10-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY src/ ./src/
COPY models/ ./models/

# Expose port
EXPOSE 8000

# Run application
CMD ["uvicorn", "src.api.endpoints:app", "--host", "0.0.0.0", "--port", "8000"]

Monitoring and Observability

Key metrics:

1. Latency: Response time percentiles
2. Throughput: Requests per second
3. Error rate: Failed requests percentage
4. Quality: Output quality scores
5. Cost: Token usage and cost per request

Continuous Improvement

Improvement cycle:

1. Monitor: Collect metrics and feedback
2. Analyze: Identify issues and opportunities
3. Plan: Prioritize improvements
4. Implement: Make changes
5. Verify: Test and validate changes

Project Documentation

README Template

# LLM Capstone Project: [Project Name]

## Overview
Brief description of the project and its goals.

## Architecture
High-level architecture diagram and component descriptions.

## Setup
Instructions for setting up the development environment.

## Usage
Examples of how to use the system.

## Evaluation
Results of evaluation against requirements.

## Deployment
Instructions for deploying to production.

## Future Work
Potential improvements and extensions.

Model Card

Best Practices

Project Management

1. Agile methodology: Use sprints and iterative development
2. Version control: Use Git for code and documentation
3. Regular reviews: Demo progress regularly
4. Risk management: Identify and mitigate risks early

Code Quality

1. Code review: Review all code before merging
2. Testing: Maintain high test coverage
3. Documentation: Document code and decisions
4. Refactoring: Continuously improve code quality

Learning

1. Reflection: Reflect on what you learned
2. Knowledge sharing: Share findings with others
3. Portfolio: Document the project for your portfolio
4. Presentation: Prepare a presentation of your work

Practice Exercises

1. Project Planning: Create a detailed project plan for your capstone project, including milestones, risks, and mitigation strategies.

2. Architecture Design: Design the system architecture for your project, including component diagrams and data flows.

3. Implementation Sprint: Implement the core functionality of your project in a time-boxed sprint.

4. Evaluation: Conduct a comprehensive evaluation of your system against requirements.

What to Learn Next