This post explores how LLM-driven systems transform simple travel predictions into something intelligent, accurate, and responsive. Classic ETA systems rely on basic formulas: distance, average speed, maybe a historical table showing usual delays. These approaches are serviceable in predictable conditions but often fall short. Unexpected traffic jams, road closures, unusual…
In today’s world, knowing exactly when a delivery or a bus will arrive is very important for both companies and customers. This is where ETA, or Estimated Time of Arrival, comes in. Traditional methods for predicting ETAs are often simple and don’t always account for real-world factors such as traffic…
Prompt injection attacks are a growing problem in AI tools like chatbots and language models. They happen when someone adds or “injects” extra instructions or harmful content into a prompt to manipulate the AI. Learning how to protect AI systems from these attacks is important for anyone who builds or…
A vector database is a specialized system for storing and searching high-dimensional data represented as vectors. In simple terms: It acts as a storage space for embeddings (numeric representations), which might come from texts, images, or audio. The main job of a vector database is to quickly find which stored vectors are most similar…
Modern natural language processing allows developers to choose between small fine-tuned language models and large general-purpose LLMs like GPT-4 or LLaMA. Both solutions have their strengths and trade-offs. Small Fine-Tuned Models Small fine-tuned models, sometimes called SLMs (Small Language Models), have fewer parameters—from several million up to a few billion. They are first trained…
Retrieval-Augmented Generation (RAG) chatbots use large language models (LLMs) plus a search system that pulls information from external sources to answer questions more accurately and reliably. While powerful, RAG chatbots can hit snags—from missing answers to confusing responses. Here’s a beginner-friendly, step-by-step guide for debugging these chatbots to help make…
Large Language Models (LLMs) are powerful tools. They can understand natural language, generate text, write code, and much more. However, classic rule-based NLP (Natural Language Processing) systems—where humans program the logic and rules—are still very useful in many situations. Rule-based NLP uses a set of pre-written instructions to process language….
Retrieval-Augmented Generation, or RAG, is a smart way to make Large Language Models (LLMs) better at answering questions by giving them access to fresh and accurate information from external sources. Instead of relying only on what the model learned during training, RAG adds relevant facts from a trusted knowledge base…
Evaluating the performance of Large Language Models (LLMs) requires using a variety of metrics that measure different aspects of their output quality. Since LLMs serve a wide range of applications—such as summarization, question answering, translation, and dialogue—it is essential to select metrics appropriate to the specific task. Here is a…
Fine-tuning a Large Language Model (LLM) can be described as taking a powerful pre-trained model and training it a bit more, using examples related to your specific task, so it performs better on that task. Instead of starting from zero, you adapt the model to be good at things like…