A few years ago, most AI models ran out of context after a short conversation. Today, leading models hold one million tokens or more. This guide breaks down context length in LLMs, how tokens work, what the lost in the middle effect means for output quality, and when RAG outperforms long context.
Three weeks into testing, a learner told me my AI tutor gave her the wrong answer.
Not obviously wrong — just outdated enough to mislead.
That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most current one. And in a knowledge base that changes constantly, that’s a serious flaw.
The fix wasn’t in the retriever or the model. It was in the gap between them.
I built a temporal layer that filters expired facts, boosts time-sensitive signals, and makes the system prefer what’s still true — not just what matches.
The post RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production appeared first on Towards Data Science.
Three companies reportedly pressed US senators for changes to a crypto bill, removing language that would require them to offer trading on tokens “not readily susceptible to manipulation.”
Using Claude Code in large projects can lead to skyrocketing token costs. A 2025 Stanford study reveals developers waste thousands of tokens daily, draining budgets as unchecked context limits pile up. By setting strict boundaries from the outset, teams can reduce costs without compromising code quality. Optimizing token usage and context window sizes early on […]
The post 23 Tips for Smart Claude Code Token Saving and Workflow Optimization appeared first on Analytics Vidhya.
RAG is a model that connects large language models to live agency knowledge bases — enabling grounded, mission-specific responses, rather than generic outputs.
Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single […]
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Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach users.
The post RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time appeared first on Towards Data Science.
Modern AI systems struggle with memory. They often forget past interactions or rely on Retrieval-Augmented Generation (RAG), which depends on constant access to external data. This becomes a limitation when building assistants that need both historical context and a deeper understanding of users. MemPalace offers a different approach, enabling structured, persistent memory with higher precision […]
The post MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG appeared first on Analytics Vidhya.
As of 1st June 2026, GitHub Copilot will charge its users on the basis of the tokens they use, rather than a flat rate subscription model. The model that’s seeing the shutters closed on it is, or rather was, simple to understand and use. Users were given a set number of ‘Premium Requests’ according to […]
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