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few-shot-prompting

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Leveraged the power of Google Cloud's Vertex AI platform to develop advanced Large Language Models (LLMs). Utilizing the Python API provided by Google Cloud, this endeavor represents a significant stride in the realm of natural language processing and LLMs.

  • Updated Nov 28, 2023
  • Jupyter Notebook

Dynamic Few-Shot Prompting is a Python package that dynamically selects N samples that are contextually close to the user's task or query from a knowledge base (similar to RAG) to include in the prompt.

  • Updated Jul 12, 2024
  • Python

Dynamic Few-Shot Prompting for Customer Support AI Agents A practical implementation of dynamic few-shot prompting using LangChain and HuggingFace models. This repository provides an optimized approach to improving AI agent performance for customer support tasks by selecting relevant examples based on user queries, thus enhancing response accuracy

  • Updated Nov 23, 2024
  • Python

This repository contains results from my MSc. thesis on "Test Case Generation from User Stories using Generative AI Techniques with LLM Models." Each folder includes generated test cases in PDF, detailed metrics scores of data in Excel sheets, and visual graphs, offering a comprehensive view of the experiments in images folder and their outcomes.

  • Updated Sep 9, 2024
  • Jupyter Notebook

This repository contains Jupyter notebooks demonstrating various generation tasks with Large Language Models (LLMs). It provides examples for summarization, text generation, few-shot learning, and translation, utilizing different LLM APIs to showcase the capabilities of multiple providers.

  • Updated Nov 14, 2024
  • Jupyter Notebook

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