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 about LLM Apps in Production, let's
It's been some time since Llama 2's celebrated launch and we've seen the dust settle a bit and real use cases come to life. In this blog post, we answer frequently asked questions on Llama 2's capabilities and when should you be using it. Let's dive in! What is Llama
Portkey is building a full-stack LLMOps platform that empowers AI builders to productionize their Gen AI apps reliably and securely.
Portkey's analytics 2.0 give our users complete visibility into their LLM calls across requests, users, errors, cache and feedback.
In this blog post, we explore a roadmap for building reliable large language model applications. Let’s get started!
Learn how to use the eval framework to evaluate models & prompts to optimise LLM systems for the best outputs.
Choosing an LLM from 20+ models available today is hard. We explore Elo ratings as a method to objectively rank and pick the best performers for our use case.
The paper proposes a new decoding strategy called self-consistency to improve the performance of chain-of-thought prompting in language models for complex reasoning tasks. Self-consistency first samples a diverse set of reasoning paths and then selects the most consistent answer by marginalizing ou
The paper explores prompt tuning, a mechanism for learning soft prompts to condition frozen language models for specific downstream tasks. The approach outperforms GPT-3's few-shot learning and becomes more competitive with scale. Prompt tuning confers benefits in robustness to domain transfer and
The paper proposes a novel method called P-tuning, which employs trainable continuous prompt embeddings to improve the performance of GPTs on natural language understanding (NLU) tasks. The method is shown to be better than or comparable to similar-sized BERTs on NLU tasks and substantially improve