10 Days: Prompt Engineering, Generative AI and Data Science

Explore the basics of prompt engineering, learn how AI language models like transformers process language with attention, and practice one-shot, few-shot, and chain-of-thought prompts in LM Studio.

Explore few-shot prompts in practice with LM Studio, comparing to regular prompts, and learn how to generate structured outputs with a consistent writing style, format, and vocabulary.

Explore how large language models learn from few-shot prompts—zero, one, and few-shot learning—alongside scaling laws and long-form factuality, with safety measures and bias considerations.

Practice chain-of-thought reasoning with LM Studio by solving reasoning problems and comparing to GPT-4, using think step by step and question-first prompts to improve accuracy.

Explore tokenization and system messages to steer large language models, adjust temperature and top_p, and craft prompts that reveal bias, randomness, and accuracy.

Explore tokenization quirks and bias in language models through hands-on prompts in prompt engineering, using rock-paper-scissors, dice, and strawberries to reveal how prompts shape ai behavior.

Practice handling system messages and privacy rules by refusing to reveal a name, using the Diego Sanchez example, in a breaking the system message exercise.

Explore LM Studio parameters such as temperature, top k, top p, and repeat penalty to balance randomness and precision in LLM outputs, with notes on presence and frequent penalties.

Master prompt engineering basics from tokenization and system messages to tuning llm parameters like temperature and top-p with concise prompts. Reflect on progress, troubleshoot iteratively, and envision innovative ai applications.

Explore how to improve AI reasoning and curb hallucinations through meta prompting, structured response patterns, analogical reasoning prompting, rephrase questions, anchored sources, multi persona collaborations, and emotion prompts.

Learn to improve reasoning and reduce hallucinations through meta prompting, syntax emphasis, analogical reasoning, and rephrasing, while grounding outputs in sources and using multi‑persona collaboration and emotion prompts.

Learn how reasoning LLMs use internal chain-of-thought planning and hidden reasoning tokens to improve accuracy in multi-step tasks like coding and math, with slower but more reliable outputs.

Explore how Apple's research shows large language models struggle with genuine math reasoning, as GSM 8-K variations and the GSM symbolic template expose reliance on pattern matching.

Generate text using the OpenAI API by designing system and user prompts to craft compelling emails, adopting one-sentence paragraphs, funny hooks, and copywriter-style prompts.

Explore building reliable OpenAI API prompts using few-shot examples to craft emails, analyze writing style, and design a structured message flow with system, user, and assistant roles.

Iteratively improve the system prompt in Python to guide a rock-paper-scissors agent toward a single word move. Experiment with randomness and temperature to study bias and output behavior.

Build a rock, paper, scissors game in Python by defining a winner function and a play game loop that tracks rounds, moves, and a for tat strategy.

Analyze images from links with Python by using the OpenAI API to extract wine bottle information from an image URL, leveraging vision capabilities and GPT-4o mini.

Explore ensemble learning and the random forest, a robust, versatile ensemble of decision trees trained on random data and features to improve accuracy, handle missing values, and model non-linear relationships.

Understand how a decision tree splits data to maximize information and entropy, and how random forests combine many trees for classification, with notes on regression using frequencies and averages.

Set up the random forest workflow in Google Colab, connect to Google Drive, import numpy, pandas, matplotlib, seaborn, load the customer survey data, and refer to its data dictionary.

Explore the bias-variance trade-off by comparing overfitting and underfitting, and learn how cross-validation helps find the sweet spot for reliable test-set performance.

Evaluate an imbalanced XGBoost model by generating predictions, applying a 0.5 threshold, and analyzing KPIs with a confusion matrix, a classification report, AUC, and AUC PR to optimize outcomes.

Explore shap dependence plots to visualize interactions between age and housing, and interpret single-prediction force plots with positive and negative contributions.

Explore creating SHAP waterfall plots to visualize feature shifts and perform cohort analysis with age-based cohorts, using Shap values and summary plots for deeper insights.

Inspect data structure with str, identify numeric and character variables, convert non-numeric to dummy variables for xgboost, and create a numeric-only data set using the player package.

Explore summary statistics and a correlation matrix for a numerical data set, identify potential outliers, and discuss how xgboost's non-linearity mitigates their impact while noting multicollinearity considerations.

Evaluate a model in R using a confusion matrix to compute accuracy, sensitivity, and specificity, and interpret business implications of predicting positives versus negatives.

Configure cross-validation parameters in R using trainControl, set method to cv, enable parallel processing, and choose a five-fold split for parameter tuning.

Run the final XGBoost model in R using tuned hyperparameters from the earlier steps, replacing model one with model three and validating the results for business-ready performance.