Complete Guide to Using Large Language Models: From ChatGPT to Advanced AI Tools

Large language models have evolved from simple text generators into powerful multimodal assistants. This guide walks you through everything you need to know to use these tools effectively, from basic interactions to advanced features like voice mode and code generation.

Understanding What You’re Talking To

When you interact with ChatGPT or similar models, you’re essentially communicating with a compressed version of the internet. Think of it as a one-terabyte zip file containing roughly one trillion parameters that represent knowledge from web documents.

The model has two key components:

Pre-training knowledge: Information from internet documents, typically 6-12 months old
Post-training personality: The helpful assistant behavior programmed through human feedback

This means the model has vast general knowledge but limited recent information. It’s a self-contained entity with no built-in access to calculators, web browsers, or other tools unless explicitly provided.

Choosing the Right Model

Different pricing tiers give you access to different models:

Free Tier: Usually provides access to smaller models like GPT-4o mini Paid Tiers: Access to flagship models like GPT-4o, Claude 3.5 Sonnet, or Gemini 2.0 Pro

Larger models offer:

Better world knowledge
More creative writing
Fewer hallucinations
Superior reasoning abilities

Always check which model you’re using. The interface should display this information, though some providers don’t make it clear for free users.

Essential Conversation Management

Every conversation exists in a “context window” - the model’s working memory. When you start a new chat, this window resets to zero.

Best Practice: Start a new conversation when switching topics. Keeping irrelevant information in the context window:

Distracts the model
Slows response time
Increases costs slightly

Think of the context window as precious working memory. Keep it focused and relevant.

Thinking Models: When Problems Get Complex

Recent advances have produced “thinking models” trained with reinforcement learning. These models can reason through complex problems by generating internal thoughts before responding.

When to use thinking models:

Math problems
Complex coding challenges
Multi-step reasoning tasks
When initial responses seem insufficient

When regular models suffice:

Simple questions
Travel advice
Basic writing tasks
Quick factual queries

Look for models labeled with “reasoning,” “thinking,” or names like O1, O3, or Deep Seek R1.

Tool Integration: Beyond the Zip File

Modern LLMs can use external tools to overcome their knowledge limitations:

Internet Search

Use search tools when you need:

Recent information (events from the last few weeks)
Current prices, schedules, or availability
Latest news or trending topics
Real-time data

The model will automatically search the web, visit relevant pages, and synthesize information from multiple sources.

Python Interpreter

For mathematical calculations, data analysis, or creating visualizations, models can write and execute Python code. This is essential for:

Complex calculations
Creating charts and graphs
Data processing
Statistical analysis

Deep Research

Some platforms offer extended research capabilities where the model spends 10-30 minutes conducting comprehensive research on a topic, visiting multiple sources, and producing detailed reports with citations.

Working with Documents and Files

Upload PDFs, text files, or images to have conversations about specific content. This is particularly powerful for:

Academic papers: Get summaries and ask clarifying questions Books: Read classic texts with an AI companion for better comprehension Medical reports: Understand blood test results or medical documents (always verify with professionals) Technical documentation: Break down complex technical content

Pro tip: When uploading images with text, ask the model to transcribe the content first to ensure accuracy before asking questions.

Multimodal Capabilities

Voice Interaction

Two types of voice features exist:

Speech-to-text/text-to-speech: Your voice converts to text, model responds in text, then converts back to speech
Native voice mode: The model processes audio directly, enabling natural conversation with interruptions, tone changes, and voice effects

Image Processing

Upload images for:

Nutrition label analysis
Document transcription
Meme explanations
Visual problem solving
Screenshot analysis

Video Understanding

Some models can process video feeds in real-time, allowing you to point your camera at objects and ask questions about what they see.

Code Generation and Development

For serious programming work, consider dedicated coding assistants like Cursor or Windsurf rather than web interfaces. These tools:

Have full context of your project files
Can edit multiple files simultaneously
Integrate with your development environment
Support “vibe coding” where you describe what you want and the AI implements it

Quality of Life Features

Memory

Some models can remember information across conversations, building a profile of your preferences and context over time. This makes interactions more personalized and relevant.

Custom Instructions

Modify how the model speaks to you globally. Set preferences for:

Communication style
Level of detail
Specific expertise areas
Language learning preferences

Custom GPTs

Create specialized versions for repeated tasks like:

Language learning tools
Document analysis
Specific writing styles
Domain-specific assistance

Practical Usage Tips

For knowledge queries: Use models for information that’s well-established and frequently discussed online. Always verify critical information from primary sources.

For recent events: Use search-enabled models or dedicated research tools.

For creative work: Experiment with different models to find the writing style and creativity level that matches your needs.

For technical problems: Start with regular models, escalate to thinking models for complex issues.

For learning: Use document upload features to read papers, books, or technical documentation with AI assistance.

Getting Started

Choose a primary platform (ChatGPT, Claude, Gemini, or Grok)
Understand your pricing tier and available models
Experiment with different types of queries
Learn to use search tools for recent information
Try voice features for faster interaction
Upload documents for deeper analysis
Explore specialized features like code generation or image creation

The LLM ecosystem evolves rapidly. Features move between pricing tiers, new capabilities launch regularly, and different providers excel in different areas. Start with one platform, learn its capabilities thoroughly, then explore others to find the best tools for your specific needs.

Remember: these are powerful assistants, not infallible oracles. Always verify important information, especially for high-stakes decisions involving health, finance, or safety.