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AI and generative Al can lead to major enterprise advancements and productivity gains. By offering new capabilities, they open up opportunities for enhancing customer engagement, content creation, virtual experts, process automation and optimization, and more.

RAG and Fine-Tuning are two prominent LLM customization approaches. While RAG involves providing external and dynamic resources to trained models, fine-tuning involves further training on specialized datasets, altering the model. Each approach can be used for different use cases. In this blog post, we explain each approach, compare the two and recommend when to use them and which pitfalls to avoid.

As can be inferred from their name, foundation models are the foundation upon which developers build AI applications for tasks like language translation, text summarization, sentiment analysis and more. Models such as OpenAI's GPT, Google's Gemini, Meta’s Llama and Anthropic’s Claude, are pre-trained on vast amounts of text data and have the capability to understand and generate human-like language.

Enterprises are beginning to implement gen AI across use cases, realizing its enormous potential to deliver value. Since we are all charting new technological waters, being mindful of recommended strategies, pitfalls to avoid and lessons learned can assist with the process and help drive business impact and productivity. In this blog post, we provide a number of frameworks that can help enterprises effectively implement and scale gen AI while avoiding risk.

The generative AI industry is changing fast. New models and technologies (Hello GPT-4o) are emerging regularly, each more advanced than the last. This rapid development cycle means that what was cutting-edge a year ago might now be considered outdated. The rate of change demands a culture of continuous learning and technological adaptation.

LLM evaluation is the process of assessing the performance and capabilities of LLMs. This helps determine how well the model understands and generates language, ensuring that it meets the specific needs of applications. There are multiple ways to perform LLM evaluation, each with different advantages. In this blog post, we explain the role of LLM evaluation in AI lifecycles and the different types of LLM evaluation methods. In the end, we show a demo of a chatbot that was developed with crowdsourcing.