llm-application-patterns

name

llm-application-patterns

description

This skill should be used when building production LLM applications in any language. It applies when implementing predictable AI features, creating structured interfaces for LLM operations, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality. Covers language-agnostic patterns for type-safe contracts, modular composition, multi-provider support, and production deployment.

LLM Application Patterns

Overview

Build production LLM applications using structured, testable patterns. Instead of manually crafting prompts, define application requirements through type-safe, composable modules that can be tested, optimized, and version-controlled like regular code.

Core principle: Program LLMs, don't prompt them.

This skill provides language-agnostic guidance on:

• Creating type-safe signatures for LLM operations
• Building composable modules and workflows
• Configuring multiple LLM providers
• Implementing agents with tools
• Testing and optimizing LLM applications
• Production deployment patterns

Core Concepts

1. Type-Safe Signatures

Define input/output contracts for LLM operations with runtime type checking.

When to use: Any LLM task, from simple classification to complex analysis.

Pattern (pseudo-code):

Signature: EmailClassification
  Description: "Classify customer support emails"

  Inputs:
    email_subject: String (required)
    email_body: String (required)

  Outputs:
    category: Enum["Technical", "Billing", "General"]
    priority: Enum["Low", "Medium", "High"]
    confidence: Float (0.0 to 1.0)

Best practices:

• Always provide clear, specific descriptions
• Use enums for constrained outputs
• Include field descriptions
• Prefer specific types over generic strings
• Define confidence scores when useful

See signatures.md for detailed patterns.

2. Composable Modules

Build reusable, chainable modules that encapsulate LLM operations.

Pattern (pseudo-code):

Module: EmailProcessor
  Initialize:
    classifier = Predict(EmailClassificationSignature)

  Forward(email_subject, email_body):
    return classifier.forward(email_subject, email_body)

Module composition - chain modules for complex workflows:

Module: CustomerServicePipeline
  Initialize:
    classifier = EmailClassifier()
    router = TicketRouter()
    responder = AutoResponder()

  Forward(email):
    classification = classifier.forward(email)
    ticket = router.forward(classification)
    response = responder.forward(ticket)
    return response

See modules.md for detailed patterns.

3. Predictor Types

Choose the right predictor for your task:

Predictor	Use Case	Output
Predict	Simple tasks, classification, extraction	Direct output
ChainOfThought	Complex reasoning, analysis	Reasoning + output
ReAct	Tasks requiring tools (search, APIs)	Action sequence + output
CodeAct	Tasks best solved with code	Generated code + execution

When to use each:

• Predict: Classification, entity extraction, simple Q&A, summarization
• ChainOfThought: Math problems, logic puzzles, multi-step analysis, explanations
• ReAct: Research tasks, data gathering, calculator usage, API calls
• CodeAct: Data transformation, calculations, file manipulation

4. Provider Configuration

Support for multiple LLM providers with consistent interfaces.

Provider compatibility matrix:

Feature	OpenAI	Anthropic	Gemini	Ollama
Structured Output	Yes	Yes	Yes	Yes
Vision (Images)	Yes	Yes	Yes	Limited
Image URLs	Yes	No	No	No
Tool Calling	Yes	Yes	Yes	Varies
Streaming	Yes	Yes	Yes	Yes

Cost optimization strategy:

• Development: Ollama (free, local) or gpt-4o-mini (cheap)
• Testing: gpt-4o-mini with temperature=0.0 for determinism
• Production (simple): gpt-4o-mini, claude-3-haiku, gemini-flash
• Production (complex): gpt-4o, claude-3-5-sonnet, gemini-pro

See providers.md for configuration details.

Common Patterns

Multi-Step Pipeline

Pipeline: AnalysisWorkflow
  Steps:
    1. Extract → pull structured data from input
    2. Analyze → apply business logic / reasoning
    3. Summarize → produce final output

  Forward(input):
    extracted = extract.forward(input)
    analyzed = analyze.forward(extracted)
    return summarize.forward(analyzed)

Agent with Tools

Agent: ResearchAgent
  Tools:
    - WebSearch: search the internet
    - DatabaseQuery: query internal data
    - Calculator: perform calculations

  MaxIterations: 10

  Forward(question):
    Loop until answer or max iterations:
      1. Think: what do I need to know?
      2. Act: call appropriate tool
      3. Observe: process tool result
    Return final answer

Conditional Router

Router: SmartDispatcher
  Forward(input):
    classification = classifier.forward(input)

    if classification.complexity == "Simple":
      return simple_handler.forward(input)
    else:
      return complex_handler.forward(input)

Retry with Fallback

RobustModule:
  MaxRetries: 3

  Forward(input):
    for attempt in range(MaxRetries):
      try:
        return predictor.forward(input)
      catch ValidationError:
        sleep(2 ** attempt)  # exponential backoff

    return fallback_response  # or raise

Quick Start Workflow

1. Define Your Signature

What inputs do you need? What outputs do you expect?

Signature: YourTask
  Description: "What this task does"

  Inputs:
    input_field: Type

  Outputs:
    output_field: Type

2. Create a Module

Wrap the signature in a reusable module.

Module: YourModule
  Initialize:
    predictor = Predict(YourTaskSignature)

  Forward(input_field):
    return predictor.forward(input_field)

3. Configure Provider

Set up your LLM provider with appropriate settings.

Configure:
  provider: "openai/gpt-4o-mini"
  api_key: from environment
  temperature: 0.0 for determinism, 0.7 for creativity

4. Test

Write tests that verify the module's behavior.

Test: YourModule
  Given: known input
  When: module.forward(input)
  Then: output matches expected type and constraints

Implementation Libraries

Language	Libraries
Python	DSPy, Instructor, Outlines, Marvin, Pydantic AI
TypeScript	Zod + OpenAI, Vercel AI SDK, LangChain.js
Ruby	DSPy.rb, Instructor-rb
Go	go-openai with struct validation
Rust	async-openai with serde

Reference Files

For detailed patterns, see:

File	Topics
signatures.md	Type-safe contract patterns, field types, validation
modules.md	Module composition, pipelines, state management
providers.md	Provider configuration, switching, cost optimization
testing.md	Testing strategies, mocking, determinism
optimization.md	Prompt optimization, few-shot learning, fine-tuning

When to Use This Skill

Trigger this skill when:

• Implementing LLM-powered features in applications
• Creating type-safe interfaces for AI operations
• Building agent systems with tool usage
• Setting up or troubleshooting LLM providers
• Optimizing prompts and improving accuracy
• Testing LLM functionality
• Adding observability to AI applications
• Converting from manual prompts to programmatic approach
• Debugging LLM application issues