Partha Sarathi Roy

Partha Sarathi Roy

RAG

Evaluating RAG performance: Metrics and benchmarks

Introduction RAG architectures facilitate the extraction of pertinent information, enhancing the overall quality and accuracy of generated outputs. This blog explores the intricacies of RAG architecture, focusing on the evaluation of its retrieval and generation components, the structure of effective evaluation datasets, and the metrics essential for assessing system performance.

Chain-of-Thought prompting: A guide to enhancing LLM reasoning

Chain-of-Thought prompting: A guide to enhancing LLM reasoning

Introduction This blog explores Chain-of-Thought (CoT) prompting as a powerful technique for enhancing reasoning in large language models. By guiding models to break tasks into smaller steps, CoT mirrors human problem-solving. A study on shift ciphers reveals that CoT reasoning is influenced by factors such as task probability, frequency in

LLM hallucination detection

LLM hallucination detection

Introduction Large Language Models (LLMs) such as GPT-4 and Llama2 generate human-like text, which has enabled a variety of applications. However, alongside this fluency, a major challenge remains hallucinations—situations where the model generates factually incorrect or unverifiable information. Hallucinations in LLMs are not one-dimensional but manifest in various forms,

Advanced RAG techniques

Advanced RAG techniques

Introduction LLMs excel in knowledge-intensive tasks but often struggle with niche or long-tail queries. RAG enhances LLMs by incorporating external knowledge, yet it faces a key challenge: imperfect retrieval. This occurs when retrieved information is incorrect, irrelevant, or conflicting, leading to unreliable results. In this blog, we explore an advanced

Synthetic data generation grounded in real data sources

Synthetic data generation grounded in real data sources

Introduction In this blog, we explore the paper on Source2Synth, a framework for synthetic data generation. Source2Synth addresses the common issues of low-quality synthetic data by grounding it in real-world sources and employing a multi-stage curation process. This approach enhances data quality and relevance. What is the problem with synthetic

DSPy framework

DSPy framework

DSPy is a declarative, self-improving framework for LLMs designed to streamline pipelines and aid broader LLM application development processes. The framework allows developers to define tasks and workflows in a high-level, declarative manner using their API. This process simplifies the need to specify “what needs to be done?” rather than

Agent workflow memory

Agent workflow memory

With the rapid adoption of AI agents and their workflows by enterprises and individuals alike in their daily lives, enabling agents to perform complex, long-horizon tasks with complex trajectories has become a significant challenge. The recent paper, “Agent Workflow Memory” introduces a novel approach to address this challenge by equipping

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