RAG with LLMs: Easy to POC, Hard to Iterate On.
The paradox of LLMs is that they have made POCs (Proofs of Concept) incredibly easy to create, but at the same time, they have made iterating on the product ...
Natural Language Processing (NLP)
Why bigger context windows won’t make RAG disappear.
We sometimes hear that RAG (Retrieval Augmented Generation) is destined to disappear because LLMs have an increasingly large context window and can therefore...
4 Simple Tips for Improving RAG Performance
Here are four simple tips for improving the performance of your RAG (Retrieval Augmented Generation): </p> Understand the RAG: The RAG combines a "R...
Most LLMs don’t succeed my “coco pops’ test”
Ah, really? Few good results for my Coco Pops test... The new Mistral model: surprising. Claude 2's response: legendary. Bard (PaLM2): Anthropic Cl...
How an Average LLM Can Fool Benchmarks and Match GPT-4
How can an average LLM fool benchmarks and achieve performance comparable to GPT-4? In this scientific article, researchers show how a 13 billion parameter...
The Fleeting Value of LLM Benchmarks
You can't improve what you don't measure. However, the relevance of measurement can degrade over time. Regarding LLMs or machine learning in general, we of...
Let’s talk about AI agents and LangChain…
Let's talk about AI agents and LangChain...With LLMs, you need to "code agile". Just as teams need to adapt quickly to changes, these applications must be ab...
Open source LLMs are sometimes cheaper than APIs
There are cases where using an open source LLM is cheaper than going through API calls. The main obstacle to using open source models is the inference cost a...
Most embedding models have linguistic bias
Most embedding models have linguistic bias. Whether for semantic search or LLM-powered QA (which is itself just semantic search with an LLM at the end of the...
Large Language Models Are Not One-Size-Fits-All for NLP
Using a jackhammer to crack a nut... That's kind of what we're doing when we use a large language model for a basic NLP task... What about good old TF-ID...
The Fuzzy Notion of Overfitting with GPT4 - What Does it Mean to Overfit on All Knowledge?
What could it possibly mean to overfit on 'all available knowledge'? The notion of overfitting for GPT4 is fuzzy. Does it still make sense? With the emerg...
Deploy a sentence transformer model on AWS Lambda via Docker
Today, let's dive into the beautiful (and sometimes very painful) world of deployment. This is really when things start to be very exciting as a you gener...
Cosine similarity: an Intuitive and simple explanation
Cosine similarity is one of the most important metric in Natural Language Processing. It is most often used to quantify a distance between two embedding...