> ## Documentation Index
> Fetch the complete documentation index at: https://genai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Using LLMs in Real Applications

> Explore API cost factors, model selection strategies, and application architectures

Integrating LLMs into production requires understanding the cost structures, choosing models based on performance metrics, and learning how LLMs compose the intelligence layer of modern GenAI applications.

## 1. LLM API Cost Structure

Most commercial LLM APIs use **token-based pricing**, separating charges based on text direction:

```text theme={null}
Input Tokens (Prompt + Context) + Output Tokens (Model Generation) = Total Request Cost
```

* **Input Tokens**: Cheaper. Includes system prompts, conversation history, injected context documents, and user queries.
* **Output Tokens**: More expensive (often $3\times$ to $4\times$ the cost of input tokens). Includes the tokens generated by the model.

### 1.1 Factors Affecting Application Cost

1. **Request Volume**: Total number of daily active user queries.
2. **Context Growth**: In conversational chat apps, each turn appends previous messages, causing input token size to compound.
3. **Agent Loops**: Multi-agent architectures or loop-based thinking (ReAct) can make several LLM calls for a single user query.
   > One user interaction does not necessarily equal one API call.

## 2. Choosing the Right Model Class

There is no single "best" model. Models are categorized into classes balancing speed, cost, and capability:

```text theme={null}
Lightweight / Flash Models ──> High-speed + Low Cost ──> Simple tasks (Translation, classification)
Medium / Balanced Models   ──> Balanced speed & reasoning ──> General Q&A, simple extractions
Large / Frontier Models     ──> Advanced reasoning + High cost ──> Complex coding, multi-step math
```

### 2.1 Model Evaluation Dimensions

Before selecting a model, evaluate the following requirements:

* **Task Complexity**: Does it need advanced logic or simple classification?
* **Latency**: How fast must the response stream back?
* **Context Size**: How many documents are you injecting?
* **Data Privacy**: Can you send data to external APIs, or must you host open-weights locally?
* **Tool Calling**: Does the model support function calling to run databases or APIs?

### 2.2 Model Selection Strategy

1. **Start small**: Test your task with the cheapest, fastest model class (e.g. Gemini 2.5 Flash).
2. **Establish a test dataset**: Measure model outputs against a gold standard set of answers.
3. **Scale up if needed**: Only upgrade to larger, more expensive frontier models if the lightweight model fails to hit accuracy targets.

## 3. What Can We Build with LLMs?

LLMs serve as the semantic engine for various application architectures:

* **Chatbots**: Conversational assistants for customer support, HR, or training.
* **Document Q\&A (RAG)**: Indexing local PDF manuals, code repositories, or company wikis so the model answers questions factually.
* **Summarization**: Condensing research papers, call transcripts, or legal briefs.
* **Information Extraction**: Converting unstructured text (invoices, emails, resumes) into structured JSON formats.
* **Code Assistants**: Automated code generation, refactoring, code explanation, and unit test creation.
* **Autonomous Agents**: Goal-oriented loops combining memory, planning, and tools (e.g., booking flights, editing files, or running code).

## 4. The GenAI Bootcamp Learning Path

The four modules of this bootcamp are designed to build upon each other logically:

```text theme={null}
Module 1: GENAI FOUNDATIONS
AI, Machine Learning, Deep Learning, Transformers, and LLM mechanics.
        ↓
Module 2: PROMPT ENGINEERING
Prompt Templates, Chat Dialogue structures, LCEL pipes, and Output Parsers.
        ↓
Module 3: RAG SYSTEMS
Document loading, chunking strategies, vector databases, and semantic search.
        ↓
Module 4: AGENTIC AI
Autonomy, tool usage (function calling), memory architectures, and multi-agent coordination.
```

The core progression is:

> **Understand the model $\rightarrow$ Learn to communicate with it $\rightarrow$ Give it custom knowledge $\rightarrow$ Give it the ability to act.**