Generative AI

RAG vs. Content Stuffing: Why selective retrieval is more efficient and reliable than dumping all data into a notification

RAG vs. Content Stuffing: Why selective retrieval is more efficient and reliable than dumping all data into a notification

Large context windows have dramatically increased how much information modern language models can process with a single command. With models…
Beyond Simple API Requests: How OpenAI's WebSocket Mode Is Changing the Game for Silent AI Experiences

Beyond Simple API Requests: How OpenAI's WebSocket Mode Is Changing the Game for Silent AI Experiences

In the world of Generative AI, latency is the biggest immersion killer. Until recently, building a voice-enabled AI agent felt…
Taalas replaces programmable GPUs with robust AI chips to earn 17,000 tokens per second for virtualization.

Taalas replaces programmable GPUs with robust AI chips to earn 17,000 tokens per second for virtualization.

In the advanced world of AI infrastructure, the industry has operated under one assumption: flexibility is king. We build general-purpose…
VectifyAI Launches Mafin 2.5 and PageIndex: Achieves 98.7% Financial RAG Accuracy With New Vectorless Tree Index System.

VectifyAI Launches Mafin 2.5 and PageIndex: Achieves 98.7% Financial RAG Accuracy With New Vectorless Tree Index System.

Building a Retrieval-Augmented Generation (RAG) pipeline is easy; building that doesn't hallucinate during the 10-K test is nearly impossible. For…
A Coding Guide to Implementing, Tracking, and Testing LLM Applications Using TruLens and OpenAI Models

A Coding Guide to Implementing, Tracking, and Testing LLM Applications Using TruLens and OpenAI Models

def normalize_ws(s: str) -> str: return re.sub(r"s+", " ", s).strip() RAW_DOCS = [ { "doc_id": "trulens_core", "title": "TruLens core idea",…
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