Portkey Blog (Page 22)

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OpenAI DevDay's Implications for LLM Apps in Prod

We hosted a watch party for OpenAI's DevDay on our Discord channel and had a lot of fun discussing everything new and improved that was launched. If you're just catching up, read about all the updates here on the OpenAI website. Since we're all

Building Production-Ready RAG Apps

💡This is Portkey's first collaboration with the Hasura Team. Hasura helps you build robust RAG data pipelines by unifying multiple private data sources (relational DB, vector DB, etc.) and letting you query the data securely with production-grade controls. LLMs have been around for some time now and have

GPT-4 is Getting Faster 🐇

Over the past few months, we've been keenly observing latencies for both GPT 3.5 & 4. The emerging patterns have been intriguing. The standout observation? GPT-4 is catching up in speed, closing the latency gap with GPT 3.5. Our findings reveal a consistent decline in GPT-4

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models - Summary

This paper presents a method for compressing prompts in large language models (LLMs) to accelerate model inference and reduce cost. The method involves a budget controller, a token-level iterative compression algorithm, and an instruction tuning based method for distribution alignment. Experimental

⭐️ Getting Started with Llama 2

Llama 2 is an open-source large language model (LLM) developed by Meta. See Llama 2's capabilities, comparisons, and how to run LLAMA 2 locally using Python.

How to differentiate your AI product - Jasper style!

As developers and founders, you might find yourself asking how does your startup differentiate from ChatGPT. More so, how do you convince a customer to try your product over a generic Chat client.

🌖 Announcing $3M Seed Round to Bring LLMs to Production

Portkey is building a full-stack LLMOps platform that empowers AI builders to productionize their Gen AI apps reliably and securely.

Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding - Summary

This paper introduces the Skeleton-of-Thought (SoT) method to decrease the generation latency of large language models (LLMs). SoT guides LLMs to first generate the skeleton of the answer and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point. The m

My Journey with AI-Driven Development: From Curiosity to Necessity

As I reflect upon my last 1 year coding journey, I am struck by how deeply Artificial Intelligence (AI) has woven itself into my development practices. From the likes of GitHub Copilot to the genius behind ChatGPT, my approach to writing code has undergone a transformative shift. Allow me to

⭐️ Analyze your LLM calls - 2.0

Portkey's analytics 2.0 give our users complete visibility into their LLM calls across requests, users, errors, cache and feedback.