Prompt Engineering and Generative AI – Fundamentals

• Fundamentals of Prompt Engineering and Generative AI.
• Prompt Engineering Techniques : Zero-Shot, Few-Shot and Chain-of-Thought, Tree of Thoughts
• Retrieval Augmented Generation fundamentals
• RAGAS Evaluation Framework for LLM and LangSmith
• Fine-tuning a Large Language Model
• Guardrails for validating LLM response 

In this section, the first segment provides a definition of Retrieval Augmented Generation Prompting technique, the merits of Retrieval Augmented Generation and applying Retrieval Augmented Generation to a CSV file, using the Langchain framework

In the second segment on Retrieval Augmented Generation, a detailed example involving the Arxiv Loader, FAISS Vector Database and a Conversational Retrieval Chain is shown as part of the RAG pipeline using Langchain framework.

In the third segment on Retrieval Augmented Generation, evaluation of response from a Large Language Model (LLM) using the RAGAS framework is explained.

In the fourth segment on Retrieval Augmented Generation, the use of Langsmith is shown complementing the RAGAS framework for evaluation of LLM response.

In the fifth segment, use of the Gemini Model to create text embeddings and performing document search is explained.

Section on Large Language Model Fine-tuning  :

In this section, the first segment provides a summary of prompting techniques with examples involving LLMs from Hugging Face repository and explaining the differences between prompting an LLM and fine-tuning an LLM.

The second segment provides a definition of fine-tuning an LLM, types of LLM fine-tuning and extracting the data to perform EDA (including data cleaning) prior to fine-tuning an LLM.

Third segment explains fine-tuning a pre-trained large language model on a task specific labeled dataset in detail.

Section on Guardrails for Large Language Models:

In this section, the first segment provides a definition of Guardrails as well as examples of Guardrails from OpenAI.

In the second segment on Guardrails, examples of open source Guardrail implementations are discussed with a specific focus on GuardrailsAI for extracting information from text.

In the third section, use of GuardrailsAI for generating structured data and interfacing GuardrailsAI with a Chat Model have been explained.

Each of these segments has a Google Colab notebook included.