You need to be taking advantage of your users wherever possible. It’s become a bit of a cliche that customers are your most important stakeholders. In the past, this meant that customers bought the product that the company sold and thus kept it solvent. However, as AI seemingly conquers everything, businesses must find replicable processes to create products that meet their users’ needs and are flexible enough to be continually improved and updated over time. This means your users are your most important asset in improving your product. Take advantage of that and use your users to build a better product!
Technological advancements have always been met with a mix of skepticism and fear. From the telephone disrupting face-to-face communication to calculators diminishing mental arithmetic skills, each new technology has faced resistance. Even the written word was once believed to weaken human memory.
This guide explores different levels of complexity in Retrieval-Augmented Generation (RAG) applications. We'll cover everything from basic ideas to advanced methods, making it useful for beginners and experienced developers alike.
We'll start with the basics, like breaking text into chunks, creating embeddings, and storing data. Then, we'll move on to more complex topics such as improved search methods, creating structured responses, and making systems work better. By the end, you'll know how to build strong RAG systems that can answer tricky questions accurately.
As we explore these topics, we'll use ideas from other resources, like our articles on data flywheels and improving tool retrieval in RAG systems. These ideas will help you understand how to create systems that keep improving themselves, making your product better and keeping users more engaged.
Key topics we'll explore include:
Basic text processing and embedding techniques
Efficient data storage and retrieval methods
Advanced search and ranking algorithms
Asynchronous programming for improved performance
Observability and logging for system monitoring
Evaluation strategies using synthetic and real-world data
Query enhancement and summarization techniques
This guide aligns with the insights from our RAG flywheel article, which emphasizes the importance of continuous improvement in RAG systems through data-driven iterations and user feedback integration.
I think too many llm libraries are trying to format your strings in weird ways that don't make sense. In an OpenAI call for the most part what they accept is an array of messages.
But so many libaries wanted me you to submit a string block and offer some synatic sugar to make it look like this: They also tend to map the docstring to the prompt. so instead of accessing a string variable I have to access the docstring via __doc__.
A minimum viable product (MVP) is a version of a product with just enough features to be usable by early customers, who can then provide feedback for future product development.
Today I want to focus on what that looks like for shipping AI applications. To do that, we only need to understand 4 things.
What does 80% actually mean?
What segments can we serve well?
Can we double down?
Can we educate the user about the segments we don’t serve well?
The Pareto principle, also known as the 80/20 rule, still applies but in a different way than you might think.
How can I help? Do you know anyone that could use my help? Do you know anyone that could use my services?
These are all examples of exceptionally low agency questions. Not only is it difficult to answer the question, you subject your victim to a lot of additional work and thinking in their busy day.
It's like seeing your mom sweating away busy cooking, chopping vegetables and asking "How can I help?" It's a lot of work to manage you, and it's a lot of work to think about what you can do. Now she has to consider what's in your ability, what the unfinished work is, and prioritize that versus the other.
I work with a few seed series a startups that are ramping out their retrieval augmented generation systems. I've noticed a lot of unclear thinking around what metrics to use and when to use them. I've seen a lot of people use "LGTM@Few" as a metric, and I think it's a terrible idea. I'm going to explain why and what you should use instead.
If you want to learn about my consulting practice check out my services page. If you're interested in working together please reach out to me via email
When giving advice to developers on improving their retrieval augmented generation, I usually say two things:
Look at the Data
Don't just look at the Data
Wise men speak in paradoxes because we are afraid of half-truths. This blog post will try to capture when to look at data and when to stop looking at data in the context of retrieval augmented generation.
I'll cover the different relevancy and ranking metrics, some stories to help you understand them, their trade-offs, and some general advice on how to think.
I'm not a doctor, but I did manage to double my testosterone levels in a year. I'm going to talk about what I did, what I learned, and what I think about it:
It's just a fact that male testosterone levels have been dropping for the past couple of years.
I felt like I was in a rut and I wanted to feel better, and I did.
I was such a psycho about it that I decided to go off the protocol.
Despite that, I still think every man should get their levels tested and see if they can improve them. And just understand how they feel.
As I've shared insights on building a consulting practice, marketing strategies, and referral techniques, it's important to understand the unique position of indie consulting in the broader landscape. In this post, we'll explore how indie consulting differs from traditional large-scale consulting firms and why it can offer more value to clients.
Indie consulting is fundamentally distinct from the practices of well-known institutions. For a critical perspective on these large firms, I recommend watching John Oliver's insightful critique of McKinsey or this concise TikTok video that encapsulates the issues with big consulting firms.
In contrast to these large firms, indie consulting focuses on specialized expertise, direct accountability, and long-term value creation for clients. It's about leveraging personal experience and skills to solve specific problems, rather than applying generic frameworks or strategies. This approach aligns closely with the pricing strategies and tools I've discussed in previous posts, all aimed at delivering maximum value to clients.