Overview

In this lesson, learners will understand how large language models (LLMs) generate text, the concept of token prediction, and basic strategies to control outputs like temperature, top-K, and top-P sampling. This is foundational knowledge to prepare for effective prompt engineering.

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Concept Explanation

1. LLMs as Prediction Engines

• LLMs don’t “know” or “think” like humans. They are probabilistic token predictors.
• Each token (word or piece of a word) is predicted based on previous tokens and learned patterns from training data.
• The model iteratively predicts one token at a time to build sentences, paragraphs, or documents.

Key Idea: Your prompt sets the context and constraints for the model’s predictions.

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2. Output Configuration Settings

LLM outputs can be influenced with a few core parameters:

a) Temperature

• Controls randomness:
  • Low temperature (e.g., 0–0.3): More deterministic, safer outputs.
  • High temperature (e.g., 0.7–1): More creative or varied outputs.
• Analogous to “risk vs. creativity” in human decisions.

b) Top-K Sampling

• Limits the next token to K most probable tokens.
• Lower K → more deterministic (conservative).
• Higher K → more creative (exploratory).

c) Top-P / Nucleus Sampling

• Chooses tokens from the smallest set whose cumulative probability ≥ P.
• Dynamically adjusts the candidate pool to balance creativity and reliability.

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3. Output Length Control

• LLMs generate tokens sequentially until reaching a max token limit.
• Short limits can truncate reasoning or summaries.
• Long limits may produce verbose outputs or require more computation and cost.

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4. Putting It All Together

• Temperature, top-K, top-P, and max tokens work together.
• Example:
  • Temperature = 0 → deterministic output; top-K/top-P ignored.
  • Temperature high → top-K/top-P influence which tokens are sampled.
• Effective prompt engineering requires understanding these interactions.

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Practical Examples

1. Deterministic Summarization

Prompt: "Summarize the following text in 2 sentences."
Temperature: 0
Top-K: 1
Top-P: 0.9

2. Creative Story Generation

Prompt: "Write a short fantasy story about a dragon and a wizard."
Temperature: 0.8
Top-K: 50
Top-P: 0.95
Max tokens: 300

3. Few-shot Classification

Prompt: "Classify the following movie review as Positive or Negative."
Examples:
- 'I loved the movie!' -> Positive
- 'The plot was boring.' -> Negative
Temperature: 0
Top-K: 5
Top-P: 0.9

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Hands-on Exercise

Task: Experiment with LLM output settings.

Steps:

1. Pick a short prompt (e.g., “Explain blockchain in simple terms”).
2. Generate three outputs:
   • Deterministic: low temperature, low top-K.
   • Balanced: moderate temperature, moderate top-P.
   • Creative: high temperature, high top-K/top-P.
3. Compare results for clarity, creativity, and correctness.
4. Document observations on how settings affect output quality.

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Tools & Techniques

• APIs: OpenAI GPT, Vertex AI, Claude.
• Temperature/top-K/top-P controls: Adjust for task-specific outputs.
• Max tokens: Balance length vs. cost.
• Few-shot examples: Combine with sampling controls for structured outputs.

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Audience Relevance

• Students: Understand LLM mechanics for research or experimentation.
• Developers: Optimize prompts for reliability vs. creativity in apps.
• Business Users: Adjust AI outputs for marketing, summarization, or automation tasks.

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Summary & Key Takeaways

• LLMs predict tokens one at a time; prompts set context.
• Temperature, top-K, top-P, and token limits control output randomness, creativity, and length.
• Understanding these fundamentals is essential before diving into advanced prompt engineering.
• Experimentation is key—there’s no one-size-fits-all configuration.

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This sets up the learner perfectly for Lesson 8, which can cover advanced reasoning techniques like chain-of-thought, step-back prompting, and self-consistency.

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If you want, I can draft Lesson 8 next in the AI & LLM Fundamentals zone, focusing on “How LLMs Reason and Think Step-by-Step”, which is the natural continuation.

Do you want me to proceed with that?

You said:

continue

ChatGPT said:

Great! Let’s move to Lesson 8 in the AI & LLM Fundamentals zone. This lesson will focus on how LLMs reason, handle multi-step tasks, and use advanced prompting strategies.

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Lesson 8: How LLMs Reason – Step-by-Step Thinking & Advanced Prompt Techniques

Overview

This lesson teaches learners how to guide LLMs to reason systematically rather than producing one-shot answers. You will learn step-back prompting, chain-of-thought reasoning, self-consistency, and tree-of-thought approaches to improve reasoning and multi-step problem-solving.

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Concept Explanation

1. Step-back Prompting

• Encourages the model to pause and reflect before answering.
• Useful for tasks with multiple constraints or intermediate steps.
• Example strategy: Prompt: "Before answering, list the key points you need to consider. Then provide the final answer."
• Helps reduce errors and hallucinations by forcing the LLM to structure its reasoning.

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2. Chain-of-Thought (CoT) Prompting

• Ask the LLM to reason step by step rather than jumping to a conclusion.
• Improves performance in math, logic, planning, or multi-step reasoning tasks.
• Example: Question: "If a train leaves City A at 3 PM at 60 mph and another leaves City B at 4 PM at 80 mph towards City A, when do they meet?" Prompt: "Explain step by step how to solve this problem."

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3. Self-Consistency

• Generate multiple CoT responses and choose the most consistent answer.
• Helps reduce randomness or occasional wrong answers.
• Strategy:
  1. Ask the LLM to produce 5–10 step-by-step solutions.
  2. Compare final answers.
  3. Pick the majority or most logical outcome.

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4. Tree-of-Thought (ToT) Prompting

• Explores multiple reasoning paths simultaneously, branching like a decision tree.
• Each branch represents a different approach to the problem.
• Useful for complex tasks where the first approach may not yield the best result.

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5. ReAct (Reason + Act)

• Combines reasoning with external actions.
• Example: LLM can reason, then query a database, fetch info, or update variables, then continue reasoning.
• Enables interactive, task-driven AI applications.

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Practical Examples / Prompts

1. Step-back Prompting

Prompt: "You are a finance expert. Before giving investment advice, list 3 risks associated with tech stocks. Then provide your recommendation."

2. Chain-of-Thought

Prompt: "Solve this: 24 ÷ 3 + 7. Show all steps in reasoning before giving the final answer."

3. Self-Consistency

Prompt: "Explain step by step how to find the smallest prime factor of 91. Repeat 5 times and identify the most consistent answer."

4. Tree-of-Thought

Prompt: "Generate 3 different approaches to improve customer retention in a SaaS company. Compare the pros and cons of each approach."

5. ReAct

Prompt: "You are a research assistant. Use reasoning to identify top 3 papers on AI safety, then fetch abstracts from the database, and summarize them."

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Hands-on Project / Exercise

Task: Build a reasoning prompt for a real-world problem.

Steps:

1. Choose a multi-step problem (e.g., scheduling tasks, logistics planning, or investment strategy).
2. Apply step-back prompting to outline key considerations.
3. Use CoT prompting to reason through steps.
4. Optionally, generate multiple completions for self-consistency.
5. If complex, implement a tree-of-thought to explore alternatives.
6. Evaluate outputs for correctness, completeness, and relevance.

Goal: Produce an LLM output that demonstrates structured, step-by-step reasoning instead of one-shot answers.

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Tools & Techniques

• LLM APIs: OpenAI GPT, Vertex AI, Claude.
• Prompt engineering techniques: Step-back, CoT, self-consistency, ToT, ReAct.
• Evaluation methods: Compare multiple completions, validate reasoning chains, check consistency.
• Interactive tools: LangChain or LLM agents for ReAct-style workflows.

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Audience Relevance

• Students & Researchers: Learn how to get LLMs to solve complex problems systematically.
• Developers: Implement advanced reasoning in AI applications.
• Business Users: Automate multi-step decision-making tasks with reliable outputs.

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Summary & Key Takeaways

• Step-back prompting helps the LLM plan its reasoning before answering.
• Chain-of-thought is essential for multi-step problem-solving.
• Self-consistency reduces errors in reasoning tasks.
• Tree-of-thought explores alternative reasoning paths.
• ReAct enables reasoning integrated with actions or data queries.
• Mastery of these techniques is foundational before tackling full LLM application design.