LLM Concepts Every Developer Should Know (Tokens, Context, Embeddings)

Large Language Models (LLMs) like GPT, Claude, and LLaMA are transforming how developers build applications. From chatbots to recommendation systems and intelligent assistants, understanding how LLMs work under the hood is essential for anyone building AI-driven products.

Even if you’re not a machine learning researcher, grasping a few core concepts like tokens, context, and embeddings will make it easier to design, optimise, and integrate LLMs into real-world apps.

In this guide, we’ll break down the essentials every developer should know in simple, practical terms.

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1. Tokens: The Building Blocks of LLMs

At the heart of every LLM is the concept of a token.

What Is a Token?

A token is a piece of text. It could be a word, part of a word, or even punctuation. LLMs don’t read text like humans; they read tokens.

For example:

Sentence: “I love AI.”
Tokens: [I] [love] [AI] [.]

Some languages split words differently, and LLMs may break words into subword tokens for efficiency.

Why Tokens Matter for Developers

• Cost & Usage: Most LLM APIs charge by the number of tokens processed.
• Prompt Design: Shorter, precise prompts use fewer tokens and reduce cost.
• Context Limits: LLMs can only “see” a fixed number of tokens at a time (e.g., GPT-4: 8K–32K tokens).

💡 Pro Tip: Always count tokens before sending prompts to optimise performance and avoid hitting context limits.

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2. Context: Understanding Conversations and Text

LLMs don’t just generate text randomly. They rely heavily on context, the surrounding text, to make accurate predictions.

What Is Context?

Context is the information the model “remembers” when generating a response. This can include:

• Previous messages in a chat
• Earlier paragraphs in a document
• Instructions provided in the prompt

For example:

Prompt 1: “Write a Python function to calculate factorial.”
Prompt 2 (after context): “Now modify it to handle negative numbers gracefully.”

The model uses the context to produce relevant and coherent outputs.

Why Context Matters

• Ensures coherent multi-step conversations
• Helps LLMs maintain persona or style
• Crucial for RAG (Retrieval-Augmented Generation) systems to combine external knowledge with prompt instructions

💡 Pro Tip: For long documents or chats, use embedding + vector search to extend context beyond the model’s token limit.

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3. Embeddings: Turning Text Into Vectors

If tokens are the raw input and context is the “memory,” embeddings are how LLMs understand meaning mathematically.

What Are Embeddings?

An embedding is a numeric vector representation of text. It captures the meaning of words, sentences, or documents in a high-dimensional space.

• Similar meanings → vectors are close
• Different meanings → vectors are far apart

Example:

“Dog” and “puppy” → vectors close together
“Dog” and “Rocket” → vectors far apart

Why Embeddings Are Useful for Developers

• Semantic Search: Find documents or FAQs similar in meaning to a query
• Clustering & Recommendations: Group similar items or suggest content
• RAG Systems: Combine embeddings with vector databases to provide context for LLMs beyond token limits

💡 Pro Tip: Use prebuilt embedding models like OpenAI text-embedding-3 or Hugging Face sentence transformers for faster implementation.

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4. Other Key Concepts Developers Should Know

• Attention Mechanism: The attention mechanism enables LLMs to focus on the most important parts of the input while generating outputs. It’s like highlighting relevant words to make smarter predictions.
• Fine-Tuning & LoRA: Fine-tuning adapts a pre-trained LLM to your specific use case. LoRA (Low-Rank Adaptation) allows efficient fine-tuning without retraining the entire model.
• Prompt Engineering: Learning to craft effective prompts is crucial. Small changes in wording can dramatically affect outputs.

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5. Practical Tips for Developers Using LLMs

1. Keep Track of Tokens: Monitor token usage to reduce cost.
2. Manage Context Carefully: Always include the necessary background for multi-step tasks.
3. Leverage Embeddings: For search, retrieval, and semantic understanding.
4. Test & Iterate Prompts: Try multiple variations to get accurate results.
5. Monitor Outputs: Watch for hallucinations, bias, or irrelevant responses.

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Real-World Examples

• Customer Support Chatbots: Combine embeddings + context to answer FAQs accurately.
• Code Assistants: Use token-efficient prompts for code generation with GitHub Copilot.
• Content Summarisation: Feed large documents via embeddings for context-aware summaries.
• Recommendation Systems: Semantic similarity search using vector embeddings.

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Conclusion

For developers, understanding tokens, context, and embeddings is the first step to building effective LLM-powered applications. These concepts are the foundation of everything from smart chatbots to AI-driven search and recommendation engines.

By mastering these ideas, developers can not only leverage LLMs effectively but also optimise for performance, cost, and accuracy, creating AI applications that are both powerful and practical.