AI-Assisted Coding & LLM Prompt Engineering

Introduction

This repository provides resources and demos related to AI-assisted coding, autoregressive large language models (LLMs), and advanced prompt engineering techniques.

Topics Covered:

1. AI-Assisted Coding
2. Autoregressive Large Language Models (LLMs)
3. Prompt Engineering 201

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AI-Assisted Coding

The repository contains a Jupyter Notebook (AI_ASSISTED_CODING.ipynb) demonstrating AI-assisted coding techniques using language models.

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Autoregressive Language Models

Autoregressive LLMs predict the next word based on input text and operate as probability kernels.

Key Concepts:

• Next-Word Prediction: Given a prefix, the model generates the most likely next word.
• Stochastic Nature: A given input may produce different outputs sampled from probability distributions, which can result in inconsistencies or hallucinations.
• Probability Calculations: Language models assign probabilities to sequences of words based on prior observations.

Types of Large Language Models:

1. Base LLM (Pre-Trained): Trained on large text corpora to predict the next word in a sentence.
2. Instruction-Tuned LLM (Fine-Tuned): Optimized to follow instructions and improve response accuracy using techniques such as:
   • Supervised Fine-Tuning (SFT)
   • Reinforcement Learning with Human Feedback (RLHF)
   • Direct Preference Optimization (DPO)

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Sampling and Parameters

Key Parameters Controlling Model Outputs:

• Temperature: Controls randomness (higher values = more creativity, lower values = more deterministic responses).
• Top-p (Nucleus Sampling): Limits token selection to the most probable subset.
• LogProbs: Logs probability scores for debugging and confidence analysis.
• Penalties:
  • Frequency Penalty: Reduces repetition of frequent words.
  • Presence Penalty: Encourages diverse outputs by avoiding repeated phrases.
• Max Tokens: Limits response length.
• Stop Sequences: Defines token sequences that halt text generation.
• n & best_of: Controls the number of completions generated.

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Prompt Engineering 201

The repository includes a Jupyter Notebook (PROMPT_ENGINEERING.ipynb) demonstrating prompt optimization techniques for LLMs.

Core Topics:

• Understanding Model Behavior: Fine-tuning prompts to achieve desired responses.
• Controlling Creativity & Accuracy: Adjusting parameters for structured, coherent, and relevant outputs.
• Debugging Model Responses: Using log probabilities to refine prompts.

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Demo & Usage

To explore the concepts, run the provided Jupyter Notebooks:

1. AI_ASSISTED_CODING.ipynb – AI-assisted coding demonstrations.
2. PROMPT_ENGINEERING.ipynb – Advanced prompt engineering strategies.

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References

• DeepLearning.AI – ChatGPT Prompt Engineering
• Stanford NLP (CS224N)
• AI Engineering Research

For contributions, open a pull request or create an issue in this repository. Happy coding!