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…
When working with large language models (LLMs) like GPT-4, Claude, or Gemini, you may often hear about the model’s “context window.” A context window refers to the maximum span of text (measured in tokens, which are chunks of words or characters) that an LLM can consider at one time when generating a…
Prompt engineering can be a powerful tool to minimize hallucinations in transport AI by guiding large language models (LLMs) to produce more accurate, relevant, and fact-based responses. Here are key ways prompt engineering helps reduce hallucinations in transport applications: Set Clear and Specific Instructions:Using precise language and detailed prompts helps…
Key hallucination types common in the transportation domain typically align with broader natural language generation hallucinations but have unique manifestations related to transit and traffic data. They include: Factual Hallucinations:The model generates false or fabricated transportation facts, such as incorrect traffic incident reports, wrong vehicle counts, mishandled route schedules, or…
Cross-verify With Trusted SourcesAlways corroborate key facts or figures generated by the LLM with reliable, authoritative sources such as official websites, academic papers, or verified databases. If the output contradicts these trusted references, it’s likely a hallucination. Check Logical ConsistencyReview the output for internal contradictions or implausible claims. Hallucinated content…