Data Science

  Unlocking the True Cost of Generative AI As organizations increasingly integrate generative AI into their operations, understanding the associated costs is crucial for effective budgeting and resource allocation. Here's a comprehensive breakdown of the various cost types involved in building and maintaining AI systems: GenAI Tools & Platform Access Costs Prompt Engineering Costs Inference Costs Fine-Tuning Costs Infrastructure Costs Data Management Costs Operational Costs AI Regulations Compliance Costs Talent Costs Change Management Costs By strategically managing these cost types, you can maximize the investment in generative AI and drive innovation forward. What are your thoughts on these cost factors? How are you addressing them?

 LLM Evaluation Guide Large Language Model (LLM) is the industry buzz word in recent years. It can understand human language and plays crucial roles in applications like chatbots, translations, and content creation. Evaluating LLMs is vital to ensure they produce accurate, relevant, and reliable outputs while minimizing biases and errors. Effective evaluation helps identify the strengths and weaknesses of these models, ensuring they perform well in real-world scenarios. Key metrics include BLEU and ROUGE for text quality, BERTScore and MoverScore for semantic similarity, and QuestEval for relevance and completeness. Proper evaluation guarantees that LLMs meet high standards and user expectations. Here are few dimensions on which LLMs can be evaluated. - Evaluating Generated Text Quality - Evaluating Semantic Similarity - Evaluating Factual Consistency - Evaluating Relevance and Completeness - Detecting Hallucinations - Evaluating User Preferences - No References Available What other di