README

Overview

This repository documents my work and learning journey with advanced AI techniques, utilizing a Retrieval-Augmented Generation (RAG) framework. The focus is on Prompt Engineering, Retrieval-Augmented Generation, Fine-Tuning, and Sequential and Hierarchical Agents using CrewAI, with evaluations performed using OpenAI and Groq models.

Table of Contents

1. Introduction
2. Prompt Engineering
3. Retrieval-Augmented Generation (RAG)
4. Fine-Tuning
5. Sequential and Hierarchical Agents Using CrewAI
6. Evaluations
   • OpenAI Models
   • Groq Models
7. Experiments and Results
8. Conclusion
9. References

Introduction

This project explores various AI techniques with a special emphasis on Retrieval-Augmented Generation (RAG). The goal was to develop and refine skills in Prompt Engineering, RAG, Fine-Tuning, and building Sequential and Hierarchical Agents using CrewAI. Evaluations were conducted using OpenAI and Groq models to benchmark performance and effectiveness.

Prompt Engineering

Objectives

• Design and optimize prompts for generating specific outputs from AI models.
• Refine prompts through iterative testing and feedback.

Key Activities

• Initial Prompt Design: Crafted prompts to achieve desired AI responses.
• Iterative Refinement: Adjusted prompts based on model feedback and output quality.
• Case Studies: Analyzed the impact of various prompt engineering techniques.

Results

• Developed a set of best practices for effective prompt design.
• Documented successful and unsuccessful prompt examples.

Retrieval-Augmented Generation (RAG)

Objectives

• Integrate retrieval mechanisms with generation models to enhance information relevance and accuracy.
• Experiment with different retrieval strategies and assess their impact on generation quality.

Key Activities

• RAG Model Implementation: Set up a RAG model with integrated retrieval components.
• Retrieval Strategies: Tested various approaches, including dense and sparse retrieval methods.
• Performance Assessment: Evaluated the impact of retrieval on generation quality.

Results

• Enhanced model performance for tasks requiring external knowledge.
• Documented techniques for effective retrieval and generation integration.

Fine-Tuning

Objectives

• Adapt pre-trained models to specific tasks or datasets to improve performance.
• Explore various fine-tuning methodologies and assess their effectiveness.

Key Activities

• Dataset Preparation: Curated and preprocessed datasets for fine-tuning.
• Model Training: Fine-tuned pre-trained models with different training regimes.
• Performance Evaluation: Measured improvements in model performance post fine-tuning.

Results

• Achieved notable performance gains on targeted tasks.
• Documented fine-tuning strategies and their outcomes.

Sequential and Hierarchical Agents Using CrewAI

Objectives

• Build and manage sequential and hierarchical agents for handling complex tasks.
• Utilize CrewAI for creating and orchestrating these agents.

Key Activities

• Agent Design: Developed architectures for sequential and hierarchical agents.
• Implementation: Deployed agents using CrewAI.
• Testing and Evaluation: Assessed the capabilities and performance of deployed agents.

Results

• Successfully created and deployed functional sequential and hierarchical agents.
• Provided insights into effective agent design and management with CrewAI.

Evaluations

OpenAI Models

• Evaluation Goals: Benchmarked performance of models using OpenAI’s API.
• Methods: Tested prompt engineering, RAG integration, and fine-tuning using OpenAI models.
• Findings: Provided insights into model performance and effectiveness in different scenarios.

Groq Models

• Evaluation Goals: Assessed model performance and efficiency with Groq’s hardware.
• Methods: Conducted experiments to compare results with those from OpenAI models.
• Findings: Analyzed Groq models’ strengths in handling large-scale computations and specific tasks.

Experiments and Results

• Prompt Engineering: Documented examples and effectiveness of various prompts.
• RAG Models: Performance metrics and retrieval strategies used.
• Fine-Tuning Results: Improvements and strategies for fine-tuning.
• Agents with CrewAI: Capabilities and practical outcomes of sequential and hierarchical agents.

Conclusion

This project provided a comprehensive exploration of advanced AI techniques, demonstrating practical applications and performance improvements across various methodologies. The evaluations using OpenAI and Groq models highlighted the strengths and limitations of different approaches, offering valuable insights into effective AI model utilization.

References

• CrewAI Documentation
• Retrieval-Augmented Generation (RAG) Paper
• Fine-Tuning Pretrained Models
• Prompt Engineering Techniques