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Whitepaper Companion Podcast - Prompt Engineering
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Whitepaper Companion Podcast - Prompt Engineering

Deep Dive into Prompt Engineering

Introduction to Prompt Engineering

  • The session introduces prompt engineering, emphasizing its importance in effectively utilizing large language models (LLMs).
  • Focus is on helping Kaggle users generate efficient code, debug issues, and innovate solutions through effective prompting techniques.
  • The discussion aims to equip participants with practical methodologies for prompt engineering tailored to Kaggle challenges.

Configuring LLM Outputs

  • Emphasizes that shaping the output of LLMs involves more than just input; it requires careful configuration of model parameters.
  • Output length is crucial; setting a low token limit may not suffice for concise responses in Kaggle notebooks.

Sampling Controls: Temperature Settings

  • Introduces sampling controls, explaining how LLM selects words based on probabilities and the role of temperature in this process.
  • A low temperature results in predictable outputs, ideal for generating specific code snippets like library imports.
  • Conversely, a higher temperature allows for creativity and diversity in outputs, useful for brainstorming new features or algorithms.

Fine-Tuning Word Selection: Top K and Top P

  • Discusses top K and top P as methods to refine word selection during generation.
  • Top K limits choices to the most probable candidates; higher values allow more variety while lower values focus output.
  • Top P works by selecting words whose cumulative probabilities meet a specified threshold, allowing flexibility based on set values.

Interplay Between Parameters

  • Explains how temperature interacts with top K and top P settings when generating text from an LLM.
  • If only one of top K or top P is active without temperature set, randomness is introduced from the filtered options available.

Recommendations for Effective Prompting

  • For deterministic code generation in Kaggle tasks, setting temperature to zero ensures consistent outputs while making other parameters less relevant.

Understanding Temperature Settings and Prompt Engineering in LLMs

Temperature Settings and Repetition Loop Bug

  • Lowering the temperature to around 0.1 with a top P of 0.9 can help achieve more controlled outputs, while a temperature of zero is ideal for specific algorithm implementations.
  • The repetition loop bug occurs when the model repetitively generates the same words or phrases, which can happen at both low and high temperatures due to predictability or randomness.
  • Fine-tuning sampling parameters like temperature, top K, and top P is essential to avoid repetitive outputs while still encouraging creativity in responses.

Crafting Effective Prompts

  • Clear prompt crafting is fundamental for obtaining accurate predictions from models; using specific techniques enhances results significantly.
  • General prompting (zero-shot prompting) involves providing a task description without examples, allowing the model to generate code based on its extensive training data.
  • Documenting prompts meticulously is crucial for iterative improvement in Kaggle projects; tracking what works helps refine future attempts.

Advanced Prompt Techniques: One-Shot and Few-Shot Prompting

  • One-shot and few-shot prompting involve providing examples within prompts to guide the model towards desired output formats, enhancing clarity in tasks.
  • For instance, showing input data alongside expected JSON output helps the model understand formatting requirements better.
  • The quality of provided examples is critical; poorly chosen examples can confuse the model leading to subpar outputs. Including edge cases is particularly important.

System Contextual and Role Prompting

  • System prompting sets overall context by defining roles or purposes for the model's responses, such as acting as a coding assistant for Kaggle data science tasks.
  • Specifying output requirements through system prompts ensures structured data returns that are useful for analysis and submission files.
  • Role prompting assigns a specific persona to influence response style; this can be tailored according to project needs (e.g., technical writer vs. senior software engineer).

Contextual Information and Stepback Prompting

  • Contextual prompting provides relevant background information necessary for understanding specific tasks; detailed queries yield more helpful responses from models.

Feature Engineering and LLMs in Kaggle

Activating Knowledge Base for Feature Engineering

  • The discussion begins with the importance of activating the model's knowledge base before addressing specific Kaggle problems, particularly in feature engineering.
  • By first querying general principles of feature engineering, users can derive more insightful and creative outputs tailored to their datasets.
  • This approach may also help mitigate biases present in the language model's responses.

Chain of Thought Prompting (CoT)

  • The technique known as Chain of Thought prompting is highlighted as beneficial for multi-step reasoning tasks common in Kaggle competitions.
  • CoT encourages models to articulate intermediate reasoning steps before arriving at a final answer or code suggestion, enhancing understanding of the model's thought process.
  • While CoT improves transparency and robustness, it does require more tokens, potentially increasing costs and processing time.

Self-Consistency and Reliability

  • Self-consistency builds on CoT by generating multiple reasoning paths for the same prompt, allowing users to select the most consistent answer from various outputs.
  • This method provides a consensus view akin to gathering expert opinions, which is especially useful for critical submissions in Kaggle competitions.

Exploring Advanced Techniques: Tree of Thoughts (ToT)

Branching Problem-Solving Approach

  • Tree of Thoughts (ToT) offers a complex problem-solving strategy that allows models to explore multiple reasoning paths simultaneously rather than following a linear chain.
  • This technique is ideal for open-ended challenges where there isn't one clear solution path, promoting exploration and creative problem-solving.

React: Reasoning with External Tools

Integrating LLM with External Resources

  • The React framework combines language model reasoning capabilities with external tools like search engines or APIs, enabling active participation in workflows.
  • An example illustrates how an LLM can execute code within a Kaggle notebook, analyze results, and adjust its next steps based on findings.

Automatic Prompt Engineering (AP)

Automating Effective Prompt Creation

  • Automatic Prompt Engineering involves using AI to generate variations of prompts for specific tasks and evaluating their performance.
  • This technique can streamline finding better prompts for code generation or data analysis projects within Kaggle environments.

Code Prompting Applications

Enhancing Code Development Efficiency

  • Code prompting is crucial not only for natural language tasks but also significantly benefits coding-related activities on platforms like Kaggle.
  • Key applications include writing code efficiently while emphasizing the need for careful review and testing before implementation.

Prompt Engineering Techniques for Kaggle

Translating Code and Debugging

  • Discusses the utility of translating code from one programming language to another, emphasizing the importance of verifying that the translated code functions correctly.
  • Highlights an example where a broken Python script is debugged using error traceback prompts, showcasing how LLMs can identify errors and suggest fixes.
  • Notes that LLMs can also propose enhancements for making code more robust and efficient, likening it to having a coding buddy for support.

Multimodal Prompting

  • Introduces multimodal prompting, which involves using inputs beyond text (e.g., images or audio), indicating its growing relevance in Kaggle competitions involving diverse datasets.

Best Practices in Prompt Engineering

  • Emphasizes the significance of best practices in prompt engineering to enhance effectiveness. The first practice mentioned is providing examples through one-shot and few-shot prompting.
  • Stresses the need for simplicity in prompts—keeping them clear and concise while avoiding jargon to ensure both clarity for users and understanding by LLMs.

Positive Instructions Over Constraints

  • Suggests focusing on positive instructions rather than constraints when formulating prompts; e.g., specifying allowed libraries instead of prohibiting certain ones.
  • Advises managing token length to stay within Kaggle limits while ensuring efficient processing time.

Dynamic Prompts and Experimentation

  • Recommends using variables in prompts to create dynamic requests adaptable across different datasets or tasks without rewriting entire prompts.
  • Encourages experimentation with various input formats and writing styles, including mixing classes in examples during classification tasks to avoid bias.

Adapting to AI Evolution

  • Urges keeping abreast of model updates as new features are released, suggesting that staying informed can provide a competitive edge in Kaggle challenges.

Structured Data Handling

  • Discusses working with structured data formats like CSV or JSON for data-heavy tasks, noting tools available for repairing JSON format issues if needed.

Collaboration Among Engineers

  • Highlights the value of collaboration among prompt engineers—sharing successful strategies can accelerate learning and lead to breakthroughs.

Documenting Prompt Attempts

  • Stresses the importance of documenting all prompt attempts, results obtained, and feedback received. This practice aids progress tracking and future debugging efforts.

Iterative Process of Prompt Engineering

  • Concludes that prompt engineering is an iterative process requiring continuous learning and refinement to achieve optimal results.

Kaggle Competitions: Best Practices and Techniques

Leveraging LLMs in Kaggle Projects

  • Emphasizes the importance of utilizing techniques and best practices for success in Kaggle competitions, particularly for those working on Capstone projects.
  • Encourages experimentation and iteration to push the boundaries of what can be achieved with large language models (LLMs).
  • Highlights the competitive nature of Kaggle, urging participants to adopt innovative strategies to gain an advantage.
  • Suggests that listeners should actively engage with the content presented, applying insights directly to their projects.
Playlists: 5-Day Gen AI Course 2025 - Companion Podcast Episodes
Video description

Read the whitepaper here: https://www.kaggle.com/whitepaper-prompt-engineering Learn more about the 5-Day Generative AI Intensive: https://rsvp.withgoogle.com/events/google-generative-ai-intensive_2025q1 Introduction: When thinking about a large language model input and output, a text prompt (sometimes accompanied by other modalities such as image prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most effective prompt can be complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore, prompt engineering is an iterative process. Inadequate prompts can lead to ambiguous, inaccurate responses, and can hinder the model’s ability to provide meaningful output. When you chat with the Gemini chatbot, you basically write prompts, however this whitepaper focuses on writing prompts for the Gemini model within Vertex AI or by using the API, because by prompting the model directly you will have access to the configuration such as temperature etc. This whitepaper discusses prompt engineering in detail. We will look into the various prompting techniques to help you getting started and share tips and best practices to become a prompting expert. We will also discuss some of the challenges you can face while crafting prompts.