6. Inference Techniques

Introduction

Now that we’ve explored the fundamentals of LLMs, key players, deployment considerations, technical foundations, and the art of prompt engineering, it’s time to dive into how these models actually operate in real-world applications. This section examines the technical aspects of inference – the process where LLMs generate responses to our inputs – focusing on API integration patterns, response handling, knowledge integration with RAG, and cost optimization strategies.

This section is hands-on: you’ll follow along with practical examples, experiment with different approaches, and build understanding of how to make inference work efficiently for real applications.

API Integration Types

When integrating with Large Language Models, you have two primary approaches: the Completion API and the Chat API. While both facilitate interactions with LLMs, they are designed for different types of tasks and use cases.

Completion API

The Completion API is ideal for single-turn interactions, where you send one prompt and receive one response. It offers precise control over the input structure and is best suited for isolated tasks.

Key Features:

• Simplicity: Each interaction is self-contained, with no built-in conversation structure.
• Flexibility: You can design prompts exactly as needed.
• Token Efficiency: Typically uses fewer tokens for standalone tasks.
• Use Cases: Generating content, text summarization, one-off queries.

Enhancing Completion API with Templates

Developers often employ templating techniques to improve prompt clarity:

system
You are a helpful coding tutor who explains concepts clearly.

user
What is a for loop?

assistant
A for loop is a control flow statement that repeats a block of code
a specified number of times...

user
Can you give me an example?

assistant

### System ###
You are a helpful coding tutor who explains concepts clearly.

### User ###
What is a for loop?

### Assistant ###
A for loop is a control structure used to iterate over a sequence...

### User ###
Can you give me an example?

### Assistant ###

Chat API

The Chat API is specifically built for multi-turn, interactive conversations. It organizes messages into discrete units, each assigned a role:

{
  "role": "system",
  "message": "You are a helpful coding tutor who explains concepts clearly."
},
{
  "role": "user",
  "message": "What is a for loop?"
},
{
  "role": "assistant",
  "message": "A for loop is a control structure that allows you to execute a block of code repeatedly."
},
{
  "role": "user",
  "message": "Can you show me an example?"
}

Key Features:

• Structured Dialogue: Well-defined message roles (system, user, assistant).
• Multi-Turn: Handles conversational exchanges naturally.
• Enhanced Clarity: Each message is explicitly tagged.
• Use Cases: Chatbots, interactive assistants, back-and-forth dialogue.

Choosing the Right API

Response Handling

The way you receive and process responses from an LLM significantly impacts your application’s user experience:

Knowledge Integration Strategies

When working with LLMs, you’ll need to decide how to provide the model with the information it needs. There are three main approaches, each suited for different scenarios:

1. Base Model Only – Use the model’s training knowledge directly. Simple but limited to training cutoff date.

2. In-Prompt Static Data – Include necessary information directly in the context window. Good for small, contained datasets.

3. Retrieval-Augmented Generation (RAG) – Dynamically retrieve relevant information from external sources. The foundation of most enterprise AI applications.

The RAG Pipeline: A Practical Walkthrough

RAG bridges the gap between an LLM’s static training knowledge and the dynamic information your application needs. Let’s walk through exactly how it works.

How RAG Works: The Data Flow

flowchart LR
    subgraph Ingestion["1. Document Ingestion"]
        A[Documents] --> B[Chunking]
        B --> C[Embedding Model]
        C --> D[(Vector Database)]
    end

    subgraph Query["2. Query Time"]
        E[User Query] --> F[Embed Query]
        F --> G[Similarity Search]
        D --> G
        G --> H[Relevant Chunks]
    end

    subgraph Generation["3. Response Generation"]
        H --> I[Build Prompt]
        E --> I
        I --> J[LLM]
        J --> K[Response]
    end

    style A fill:#2d5016,color:#fff
    style D fill:#1a3a5c,color:#fff
    style J fill:#4a1a5c,color:#fff
    style K fill:#2d5016,color:#fff

Step 1: Document Ingestion

Before your RAG system can answer questions, it needs to process and store your documents:

Chunking: Documents are split into smaller pieces (chunks). This is critical because:

• LLMs have context window limits – you can’t send entire documents
• Smaller, focused chunks lead to more relevant retrieval
• Chunk size affects both retrieval quality and cost

Embedding: Each chunk is converted into a vector (embedding) that captures its semantic meaning. The embedding model (like OpenAI’s text-embedding-3-small or open-source alternatives like BGE-M3) converts text into a high-dimensional numerical representation where similar meanings are near each other in vector space.

Storage: The embeddings are stored in a vector database (Pinecone, Weaviate, Chroma, pgvector, etc.) along with the original text chunks and any metadata.

Step 2: Query Time

When a user asks a question:

1. Embed the query: The same embedding model converts the user’s question into a vector
2. Similarity search: The vector database finds the chunks whose embeddings are most similar to the query embedding (using cosine similarity or other distance metrics)
3. Retrieve top results: Typically the top 3-10 most relevant chunks are returned

Step 3: Response Generation

The retrieved chunks are combined with the user’s question into a prompt:

You are a helpful assistant. Use the following context to answer the
user's question. If the answer is not in the context, say so.

Context:
[Retrieved chunk 1]
[Retrieved chunk 2]
[Retrieved chunk 3]

User question: What is the company's return policy for electronics?

The LLM then generates a response grounded in the retrieved information, significantly reducing hallucination risk.

Technical Components

Vector Operations and Embeddings

To understand how LLMs and RAG systems process text, we need to grasp two fundamental concepts: vectors and embeddings.

What is a Vector?

A vector is simply a list of numbers that can represent a point in space:

• A 2D vector [3, 4] represents a point 3 units east and 4 units north
• A 3D vector [1, 2, 3] represents a point in three-dimensional space

In LLMs, we use vectors with hundreds or thousands of dimensions. But at their core, vectors are just containers for numbers – they don’t inherently mean anything.

What is an Embedding?

An embedding is a specific use of vectors for representing meaning. What makes embeddings special is that:

1. They are learned through training – the model learns what numbers to put in each vector
2. They have meaningful relationships – similar concepts get similar numbers

# These same vectors become embeddings when trained to represent words:
"cat"  = [0.2, 0.5, 0.1]  # Embedding for "cat"
"dog"  = [0.3, 0.4, 0.2]  # Embedding for "dog"

# Their similarity reflects that both are pets
# Their difference reflects that they're different species

Cost Optimization Techniques

One of the most practical aspects of inference is managing costs. Token-based pricing means every interaction has a direct cost, and these costs can escalate quickly at scale.

The Cost Optimization Decision Tree

flowchart TD
    A[Incoming Request] --> B{Simple or Complex?}
    B -->|Simple| C{Cached?}
    B -->|Complex| D{Needs latest data?}

    C -->|Yes| E[Return cached response]
    C -->|No| F[Use small model<br/>GPT-4o mini / Haiku / Flash]

    D -->|Yes| G[RAG + capable model<br/>GPT-4o / Sonnet]
    D -->|No| H{Needs reasoning?}

    H -->|Yes| I[Reasoning model<br/>o3 / R1]
    H -->|No| G

    F --> J[Cache response]
    G --> J
    I --> J

    style E fill:#2d5016,color:#fff
    style F fill:#1a3a5c,color:#fff
    style G fill:#4a1a5c,color:#fff
    style I fill:#8b0000,color:#fff

Key Optimization Strategies