K-Nearest Neighbor (KNN) is a simple yet effective machine learning algorithm that makes predictions based on proximity in feature space. This blog explains how KNN works, its strengths in handling tasks like customer segmentation, medical diagnostics, and fraud detection, and its limitations with scalability, noise, and high-dimensional data. It also compares KNN with other algorithms like Decision Trees, SVMs, and Neural Networks, helping readers understand when KNN is the right choice for their applications.

Explore RAG Prompting techniques to reduce hallucinations and enhance factual accuracy in Retrieval-Augmented Generation systems. It highlights how different chain types (stuff and map_reduce) and prompt engineering methods, such as context highlighting, step-by-step reasoning, and fact verification, impact the quality of AI outputs. The blog also evaluates these prompts using metrics like BLEU, ROUGE-L, and BERT Score, showcasing how specific techniques can improve coherence and grounding. It concludes that context highlighting is highly effective across both chain types, ensuring accurate and reliable responses.

This blog explores Prompt-Based LLMs and their ability to enhance AI performance through optimized input prompts. It highlights techniques like zero-shot, one-shot, and few-shot prompting, showing how fine-tuned prompts improve accuracy, efficiency, and task adaptability without extensive retraining. Applications span code generation, customer support, data analysis, and creative writing, demonstrating their versatility across industries. The blog emphasizes prompt engineering as a critical skill for maximizing LLM potential, with best practices like iterative testing, reusable templates, and domain-specific prompts. By leveraging these approaches, businesses can unlock scalable, high-performing AI solutions for real-world use cases.