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Introduction Reranking in RAG Pipelines helps AI systems go beyond simply finding documents. A retrieval system may return several relevant documents, but the most useful information is not always ranked first. Reranking adds another step that evaluates the retrieved results and places the most useful content at the top, giving the language model better context... Continue Reading →
Introduction Hybrid Search for RAG combines keyword search and vector search to help AI applications retrieve more relevant information. Vector search is good at understanding meaning, while keyword search is useful when exact terms, names, product codes, or technical phrases matter. By using both approaches together, RAG systems can improve retrieval quality and provide LLMs... Continue Reading →
Introduction Continuous Batching for LLM Inference helps AI platforms serve many users efficiently without making every request wait for an entire batch to finish. Unlike traditional static batching, requests can enter and leave the processing batch dynamically as generation progresses. This helps GPUs stay productive, increases throughput, and gives production AI systems better control over... Continue Reading →
Introduction Constrained Decoding helps AI applications generate outputs that follow predefined structural rules instead of simply asking an LLM to follow formatting instructions. While prompting a model to “return valid JSON” can work, production systems often need stronger guarantees. By restricting which tokens the model can generate at each step, grammar-based generation can produce dependable... Continue Reading →
Introduction Small Language Models for Business can be a better choice than massive general-purpose models when an organisation needs AI for a focused and repeatable task. Instead of using billions of parameters for every request, businesses can deploy smaller models designed or adapted for classification, extraction, summarisation, routing, and other narrow workflows. For the right... Continue Reading →
Introduction Mixture of Experts Models are an AI architecture designed to increase model capacity without using every parameter for every request. Instead of activating the entire network for each token, the model contains specialised components called experts, and a routing mechanism selects only a subset of them during processing. This allows AI systems to have... Continue Reading →
Introduction Knowledge Distillation for LLMs helps smaller language models learn useful capabilities from larger and more capable models. Instead of deploying a massive model for every request, teams can use a larger model as a teacher to help create or train a smaller student model for specific tasks. The result can be an AI system... Continue Reading →
Introduction Fine-Tuning vs In-Context Learning is an important decision for teams building customized AI applications. Both approaches can make an LLM better suited to a particular task, but they work very differently. Fine-tuning changes the model's learned behaviour through additional training, while in-context learning guides the model at request time using instructions, examples, and relevant... Continue Reading →
Introduction LLM Quantization helps businesses run large language models using less memory and computing power by representing model parameters with lower numerical precision. Techniques such as 8-bit and 4-bit quantization can significantly reduce memory requirements, making it possible to deploy capable AI models on more affordable hardware. However, lower precision can introduce trade-offs, so choosing... Continue Reading →
Introduction Long Context Windows allow AI models to process much larger amounts of information in a single request, making it possible to analyse lengthy documents, conversations, codebases, and business records together. But more context does not automatically mean better answers. When too much irrelevant information is included, AI may struggle to identify what actually matters,... Continue Reading →
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