Using Large Language Models, Generative AI has revolutionized how businesses and developers tackle problems that involve natural language processing. Two popular strategies for tailoring these models to specific needs are Retrieval-Augmented Generation (RAG) and Fine-Tuning. Both approaches have distinct advantages and limitations, making the choice between them highly context-dependent.

This blog explores when to use RAG versus Fine-Tuning by diving deep into their core mechanisms, pros and cons, and practical use cases.

Fine-tuning a foundational AI model with domain-specific data can significantly enhance its performance on specialized tasks. This process tailors a general-purpose model to understand the nuances of a specific domain, improving accuracy, relevance, and usability. However, creating a fine-tuned model is only half the battle. The critical step is assessing its performance to ensure it meets the intended objectives.

This blog post explores how to assess the performance of a fine-tuned model effectively, detailing evaluation techniques, metrics, and real-world scenarios.

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As artificial intelligence continues to evolve, chatbots are becoming increasingly sophisticated in handling complex conversations. A critical factor in enhancing chatbot performance is memory—the ability to retain and leverage information from prior interactions. Memory techniques enable chatbots to provide contextually aware, personalized, and consistent responses, making conversations more meaningful and efficient.

In the rapidly advancing field of artificial intelligence, Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to enhance language models. RAG integrates retrieval-based methods with generation-based methods, enabling more informed and context-aware responses. While RAG has revolutionized many applications like customer support, document summarization, and question answering, it isn’t without limitations.

This blog will explore what RAG is, how it works, its shortcomings in delivering highly accurate results, and alternative strategies to improve precision for your queries.

As organizations increasingly seek efficient ways to harness knowledge, search technologies have evolved to meet the growing demands of users. Two prominent options have emerged: search engines and vector databases. Both serve as tools for retrieving information, but they operate on fundamentally different principles and are suited to different use cases.

This blog post will delve into the differences and advantages of using search engines versus vector databases for knowledge search. By the end, you’ll have a clear understanding of when to use each and how they can complement one another.

The software-as-a-service (SaaS) model has become a cornerstone for businesses in virtually every industry, providing scalable, efficient solutions for everything from project management to customer support. However, as SaaS has gained popularity, many organizations are starting to realize that one of the most common pricing models— per-seat pricing —can quickly spiral out of control as their teams grow. This pricing approach, while seemingly straightforward, can lead to skyrocketing bills and an unsustainable cost structure, especially for large organizations and enterprise-level deployments. For comparing price on build vs buy approach checkout our blogpost on comparison between build vs outsource vs buy. 

Generative AI models like GPT-4 are powerful tools for processing and generating text, but they come with a key limitation: a fixed-size context window. This window constrains the amount of data that can be passed to the model at once, which becomes problematic when dealing with large documents or data sets. When processing long documents, how do we ensure the AI can still generate relevant responses? In this blog post, we’ll dive into key strategies for addressing this challenge.