Lesson 9: LLM Applications

The Application Landscape

LLMs are being used across virtually every industry. Here are the major application categories:

💬

Conversational AI

Chatbots, virtual assistants, customer support agents that can hold natural conversations.

Examples: ChatGPT, Claude, customer service bots

💻

Code Generation

Write, explain, debug, and refactor code in any programming language.

Examples: GitHub Copilot, Cursor, CodeWhisperer

📝

Content Creation

Write articles, emails, marketing copy, social media posts, and creative writing.

Examples: Jasper, Copy.ai, Notion AI

🔍

Search & Research

Answer questions, summarize documents, extract information from large corpora.

Examples: Perplexity, Elicit, Consensus

📊

Data Analysis

Interpret data, generate insights, create visualizations from natural language descriptions.

Examples: Code Interpreter, Julius AI

🎓

Education

Tutoring, explaining concepts, generating practice problems, grading assistance.

Examples: Khanmigo, Duolingo Max

⚖️

Legal & Compliance

Contract review, legal research, compliance checking, document drafting.

Examples: Harvey, CoCounsel

🏥

Healthcare

Clinical documentation, medical Q&A, drug interaction checking, patient communication.

Examples: Nuance DAX, Glass Health

Common Architecture Patterns

1. Direct API Usage

Simplest pattern: send user input to LLM, return response.

User Input

→

Prompt Template

→

LLM API

→

Response

2. RAG (Retrieval-Augmented Generation)

Retrieve relevant documents, then generate answer based on them.

User Query

→

Vector Search

→

Retrieved Docs

→

LLM

→

Answer

3. Agent Systems

LLM can use tools, make decisions, and take actions autonomously.

User Goal

→

LLM Planner

→

Tool Use

↺

LLM Reasoning

→

Result

4. Multi-Step Pipelines

Break complex tasks into multiple LLM calls with intermediate processing.

# Example: Document summarization pipeline

Step 1: Chunk document into sections
Step 2: Summarize each chunk
Step 3: Combine chunk summaries
Step 4: Generate final summary
Step 5: Extract key takeaways
      

Building LLM Applications

Key Considerations

Latency: LLM calls take 100ms-10s. Design UX accordingly (streaming, loading states).

Cost: Pay per token. Optimize prompts, cache responses, use smaller models where possible.

Reliability: LLMs are probabilistic. Handle errors, validate outputs, have fallbacks.

Safety: Filter inputs/outputs, monitor for abuse, implement rate limiting.

Popular Frameworks

LangChain: Orchestration, chains, agents, tool integration
LlamaIndex: RAG, document indexing, retrieval
Haystack: Enterprise search and QA pipelines
Transformers (Hugging Face): Model loading, fine-tuning, inference

Case Study: Building a Support Chatbot

Architecture

Intent Classification: Route to appropriate handler
Knowledge Base Search: Retrieve relevant docs
Response Generation: Generate answer with context
Quality Check: Verify answer is helpful and safe
Escalation: Route to human if confidence is low

# Pseudocode for support bot

async def handle_query(user_query):
    # 1. Classify intent
    intent = await classify_intent(user_query)
    
    if intent == "technical_issue":
        # 2. Search knowledge base
        docs = await search_kb(user_query)
        
        # 3. Generate response
        context = format_docs(docs)
        response = await llm.generate(
            prompt=support_prompt,
            context=context,
            query=user_query
        )
        
        # 4. Quality check
        if await is_helpful(response, user_query):
            return response
        else:
            return escalate_to_human()
    
    elif intent == "billing_question":
        return await handle_billing(user_query)
    
    else:
        return await general_response(user_query)
        