How LLMs work, clearly explained (with visuals): ...

LLM Learning Experience Improvement - Addresses hallucination issues in long context scenarios to enhance trust in LLM learning [1] - Aims to reduce LLM's agreeableness, enabling it to challenge inaccuracies, which is correlated with hallucination [1] - Focuses on improving intent detection, prompting LLMs to ask clarifying questions when the user's intent is unclear or to better understand user preferences [1]

RT Avi Chawla (@_avichawla)6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

Context Engineering Overview - Context engineering is defined as the art and science of filling the context window with the right information at each step of an agent's trajectory [2][4] - The industry categorizes context engineering strategies into writing context, selecting context, compressing context, and isolating context [2][12] - Context engineering is critical for building agents because they typically handle longer contexts [10] Context Writing and Selection - Writing context involves saving information outside the context window, such as using scratch pads for note-taking or memory for retaining information across sessions [13][16][17] - Selecting context means pulling relevant context into the context window, including instructions, facts, and tools [12][19][20] - Retrieval-augmented generation (RAG) is used to augment the knowledge base of LLMs, with code agents being a large-scale application [27] Context Compression and Isolation - Compressing context involves retaining only the most relevant tokens, often through summarization or trimming [12][30] - Isolating context involves splitting up context to help an agent perform a task, with multi-agent systems being a primary example [12][35] - Sandboxing can isolate token-heavy objects from the LLM context window [39] Langraph Support for Context Engineering - Langraph, a low-level orchestration framework, supports context engineering through features like state objects for scratchpads and built-in long-term memory [44][45][48] - Langraph facilitates context selection from state or long-term memory and offers utilities for summarizing and trimming message history [50][53] - Langraph supports context isolation through multi-agent implementations and integration with sandboxes [55][56]

RT Helius (@heliuslabs)Our docs are now enhanced with AI, thanks to @mintlifyYou can ask questions around Solana/Helius RPCs, APIs, and streaming — to a smart AI modelyou can also make & test calls interactively and even copy/download the pages for your LLMsand yes we have llms.txt files! ...

Industry Shift - AI 和 LLM 掌握世界知识，教育行业面临重大转变 [1] - 学校和大学的角色将从提供信息转变为创造人际连接和情感成长的空间 [1] AI Disruption - AI 智能体正在颠覆教育 [1]

Influencing LLMs means influencing their sources. That involves building a ­loyal online following. To the relief of the big agency holding companies, it also means an enduring role for old-school PR https://t.co/giXPqyi4bG ...

Influencing LLMs means influencing their sources. That involves building a ­loyal online following. To the relief of the big agency holding companies, it also means an enduring role for old-school PR https://t.co/g7XpiHEq4l ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Check this!! You can now raise PRs by just writing English prompts.I integrated the @codegen coding agent with Slack and asked it to build a video RAG app.5 minutes later, it raised a PR with fully working code.Completely hands-off! https://t.co/bcQFp6yBDJ ...