LLM Application Patterns: From RAG to Agents

Large Language Models have opened up incredible possibilities for AI applications, but choosing the right architectural pattern can make or break your implementation. After building numerous LLM-powered applications, here are the patterns that consistently deliver results.

The LLM Application Landscape

Before diving into patterns, let's understand what we're working with. Modern LLM applications typically fall into these categories:

• Information retrieval and synthesis
• Content generation and editing
• Decision-making and planning
• Code generation and analysis
• Conversational interfaces

Each use case benefits from different architectural approaches.

Pattern 1: Retrieval-Augmented Generation (RAG)

RAG is the swiss army knife of LLM applications. It combines the reasoning capabilities of LLMs with access to external knowledge.

When to Use RAG

• You need up-to-date information not in the training data
• Working with proprietary or domain-specific knowledge
• Want to provide sources and citations
• Need to handle large knowledge bases efficiently

RAG Architecture

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

# Vector store setup
vectorstore = Chroma.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings()
)

# RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

Advanced RAG Techniques

Hybrid Search: Combine semantic similarity with keyword matching for better retrieval.

Re-ranking: Use a secondary model to improve the relevance of retrieved documents.

Query Expansion: Generate multiple query variations to improve retrieval coverage.

Pattern 2: Fine-tuning for Specialized Tasks

When you need consistent behavior and domain expertise, fine-tuning often beats prompt engineering.

When to Fine-tune

• Consistent output format requirements
• Domain-specific language or terminology
• Performance optimization for specific tasks
• Reducing prompt token usage

Fine-tuning Strategy

# Prepare training data
training_data = [
    {
        "messages": [
            {"role": "system", "content": "You are an expert code reviewer."},
            {"role": "user", "content": "Review this Python function..."},
            {"role": "assistant", "content": "This function has several issues..."}
        ]
    }
]

# Fine-tune using OpenAI's API
import openai

openai.FineTuningJob.create(
    training_file="file-abc123",
    model="gpt-3.5-turbo"
)

Pattern 3: Agent Systems

Agents can use tools, make decisions, and execute multi-step workflows. They're powerful but complex.

Agent Architecture

from langchain.agents import create_openai_tools_agent
from langchain.tools import DuckDuckGoSearchRun, Calculator

tools = [
    DuckDuckGoSearchRun(),
    Calculator()
]

agent = create_openai_tools_agent(
    llm=llm,
    tools=tools,
    prompt=prompt_template
)

agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=True
)

Agent Design Principles

Tool Selection: Provide focused, reliable tools rather than many mediocre ones.

Error Handling: Agents will make mistakes - plan for graceful recovery.

Observation: Log all agent actions for debugging and improvement.

Pattern 4: Pipeline Composition

Break complex tasks into smaller, composable steps.

Chain of Thought Processing

def analysis_pipeline(input_text):
    # Step 1: Extract key information
    extraction_prompt = f"Extract key facts from: {input_text}"
    facts = llm.invoke(extraction_prompt)
    
    # Step 2: Analyze implications
    analysis_prompt = f"Analyze implications of: {facts}"
    analysis = llm.invoke(analysis_prompt)
    
    # Step 3: Generate recommendations
    recommendation_prompt = f"Based on {analysis}, recommend actions:"
    recommendations = llm.invoke(recommendation_prompt)
    
    return {
        'facts': facts,
        'analysis': analysis,
        'recommendations': recommendations
    }

Pattern 5: Semantic Caching

Reduce costs and latency by caching semantically similar queries.

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class SemanticCache:
    def __init__(self, similarity_threshold=0.95):
        self.cache = {}
        self.embeddings = {}
        self.threshold = similarity_threshold
    
    def get(self, query):
        query_embedding = get_embedding(query)
        
        for cached_query, cached_embedding in self.embeddings.items():
            similarity = cosine_similarity(
                [query_embedding], 
                [cached_embedding]
            )[0][0]
            
            if similarity > self.threshold:
                return self.cache[cached_query]
        
        return None

Choosing the Right Pattern

Here's a decision matrix to help you choose:

| Use Case | Pattern | Complexity | Cost | Performance | |----------|---------|------------|------|-------------| | Q&A with docs | RAG | Medium | Medium | High | | Consistent format | Fine-tuning | High | High | Very High | | Multi-step tasks | Agents | Very High | High | Variable | | Simple processing | Pipeline | Low | Low | High | | High volume | Semantic Cache | Medium | Low | Very High |

Implementation Best Practices

1. Start Simple

Begin with the simplest pattern that could work. You can always add complexity later.

2. Measure Everything

Track token usage, latency, accuracy, and user satisfaction. What gets measured gets optimized.

3. Handle Failures Gracefully

LLMs are probabilistic - they will occasionally produce unexpected outputs. Plan for this.

4. Version Control Prompts

Treat prompts like code. Version them, test them, and review changes carefully.

5. Security Considerations

• Validate all LLM outputs before using them
• Sanitize user inputs to prevent prompt injection
• Implement rate limiting and abuse detection

The Future of LLM Patterns

Emerging patterns to watch:

• Multi-modal agents combining text, vision, and audio
• Collaborative AI systems where multiple LLMs work together
• Continuous learning systems that improve from user feedback
• Federated LLM architectures for privacy-sensitive applications

Getting Started

1. Identify your core use case - don't try to solve everything at once
2. Choose the simplest viable pattern - complexity can always be added later
3. Build evaluation metrics - you need to measure success objectively
4. Implement monitoring - LLM applications need specialized observability
5. Plan for iteration - your first implementation won't be your last

The key to successful LLM applications isn't just choosing the right model - it's choosing the right architectural pattern and implementing it thoughtfully. Each pattern has its place, and the best applications often combine multiple patterns to create robust, capable systems.

What LLM patterns have you found most effective in your applications? I'd love to hear about your experiences and challenges.