Understanding How LLMs Predict Tokens
Large Language Models (LLMs) generate text by predicting the next token in a sequence based on the input prompt. This process relies on probabilities, where the model assigns a likelihood to various possible next words and selects the most suitable one.
LLM Token Prediction Visualization
Current Phrase:
Next Token Predictions:
How to use this visualization:
- The current phrase is shown in the box at the top.
- Below are the four most likely next words with their probabilities.
- Click on any prediction to add it to the phrase and see new predictions.
- The color intensity of each prediction indicates its probability.
- The prediction chain will end after 10 words are reached.
- Use the "Back" button to undo your last selection.
- Use the "Reset" button to start over with the original phrase.
Note: This is a simplified simulation of how language models predict tokens. Real language models use more complex algorithms and context windows to generate predictions.
Here’s a simplified breakdown:
- Tokenization – LLMs break text into tokens (words, subwords, or characters). For example, “Hello world!” might be split into [“Hello”, “world”, “!”]
- Embedding – Each token is converted into a high-dimensional vector that captures meaning and context.
- Attention Mechanism – The model determines which parts of the input text are most relevant for predicting the next token.
- Probability Distribution – The model generates a probability distribution over all possible next tokens.
- Token Selection – The model selects the most probable token (or uses randomness, depending on settings like temperature).
This predictive process is what enables LLMs to generate coherent and contextually relevant responses. By structuring your prompts effectively, you can guide the model’s predictions to achieve better outputs.
The Importance of Context Window
Every LLM operates within a context window, which defines the maximum number of tokens the model can consider at once. If a prompt exceeds this limit, older tokens are forgotten, meaning the model loses important details from earlier parts of the conversation or document.
Why Context Window Matters
- Maintaining Coherence: If a prompt is too long and exceeds the context window, key parts of the input may be truncated, leading to disjointed responses.
- Optimizing Prompt Length: Being concise and prioritizing important information ensures the model retains the most relevant details.
- Sliding Context Issues: In models with smaller context windows, long conversations can push out earlier messages, requiring techniques like summary injections to retain context.
LLM Context Window Visualization
How Context Windows Work in LLMs:
- LLMs have a fixed "context window" - a limited number of tokens they can consider at once.
- As new tokens are processed, older ones "fall out" of the context window.
- Information outside the context window is completely forgotten by the model.
- This limitation affects the model's ability to maintain consistency in long generations.
- The model might forget important details, instructions, or references mentioned earlier.
- Larger context windows (8K, 16K, 32K, 100K tokens) allow models to "remember" more.
- But even with large windows, models eventually forget information at the beginning.
Context Window Examples
Strategies to Manage Context Window Effectively
- Summarization: Instead of feeding the full history, condense prior interactions into a concise summary before appending new queries.
- Reintroducing Key Information: If certain facts need to persist throughout a long interaction, they should be explicitly reintroduced in the prompt.
- Splitting Tasks: For complex queries, breaking them into smaller prompts and feeding partial responses back into the next query can help manage context limits.
- Using Metadata & Keywords: Instead of full paragraphs, using bullet points or keyword-based inputs helps maximize the available token space.
Understanding and optimizing the use of the context window is crucial for maintaining high-quality, relevant responses from an LLM, especially in long-form interactions.
Advanced Prompt Engineering Techniques
Prompt Engineering Maturity Model
From basic requests to sophisticated prompt engineering
Requests
Instructions
Formats
Techniques
Refinement
Advanced Prompt Engineering Techniques
1. Few-Shot Learning (In-Context Learning)
Instead of providing a single instruction, you can improve responses by giving examples within the prompt. This method helps the model understand the format and logic of the task.
Q: If a train travels at 60 km/h for 2 hours, how far does it go? A: Let's think step by step. 1. The speed of the train is 60 km/h. 2. It travels for 2 hours. 3. Distance = Speed x Time. 4. 60 km/h x 2 h = 120 km. Answer: 120 km.
The model continues the pattern correctly based on the examples given.
2. Chain-of-Thought (CoT) Prompting
For complex reasoning tasks, LLMs benefit from step-by-step reasoning. By prompting them to “think step by step,” accuracy improves significantly
You are a medical expert. Explain the symptoms of dehydration in simple terms.
Adding “Let’s think step by step” encourages the model to logically work through the problem rather than jumping to an answer.
3. Role-Based Instructions
By defining a role for the AI, you can guide its responses in a more structured manner.
Translate the following phrases into French: 1. Hello, how are you? -> Bonjour, comment ça va? 2. Good morning! -> Bon matin! 3. Have a great day! ->
This sets the context, leading the model to generate more domain-specific and reliable answers.
4. Instruction-Based Prompting
Providing explicit and detailed instructions in a structured way improves the quality of responses. Instead of vague requests, well-crafted instructions ensure clarity and specificity.
Write a professional email response to a customer asking for a refund. Be polite, acknowledge their issue, and provide the next steps for processing their refund.
This approach reduces ambiguity, helping the model align its response with the intended goal.
5. Formatting and Structural Prompts
Structuring a prompt using lists, tables, or templates makes responses more consistent and useful.
Summarize the following article in bullet points: - Key Points: - Main Argument: - Conclusion:
By formatting the request, the model produces structured outputs rather than a free-flowing response.
6. Prompt Iteration and Refinement
Prompt engineering is often an iterative process. If an output is unsatisfactory, modifying and testing different phrasing can lead to better responses. Some refinements include:
- Adjusting specificity: “Give a general overview of X” vs. “Explain X in detail with real-world examples.”
- Changing the tone: “Write in a casual, friendly tone” vs. “Write in a formal, academic style.”
- Breaking down complex tasks: Instead of “Write an essay on climate change,” try “List five causes of climate change and then expand on each one.”
Key Takeaways for Effective Prompt Engineering
- Be explicit – Give clear instructions and context.
- Provide examples – Use few-shot learning to show the model how to structure responses.
- Guide reasoning – Chain-of-thought prompting improves accuracy for complex tasks.
- Use roles – Assign a persona to refine responses.
- Format strategically – Use lists, templates, or structured layouts to control the response format.
- Manage context window – Optimize token usage and prevent loss of important details.
- Iterate and refine – Test and modify prompts based on response quality.
By applying these methods, you can significantly enhance the effectiveness of AI-generated responses and achieve more accurate, contextually aware outputs.
