My overall impression, after more time working with Amazon Q, is that it will take some work for a coding agent to make me faster and more effective. Q definitely removes some of the boring bits of coding (it’s great at Maven dependencies) but it’s more wayward on complicated tasks. There’s a lot to learn here.

At the end of last weekend, I’d settled on a method: writing a specification for an area of my application, having Q produce a BDD feature file outlining the behaviour, and then getting Q to fill in the testing code and after that, the implementation. This soon ran into problems as I’d still set Q too wide a brief, and the code produced quickly sprawled. There were many minor issues, such as Q producing unfocussed Cucumber step files. Along with the pages of code, some chunks of functionality were left out to ‘fill in later’.

It’s tricky to find a regular working pattern with good DevEx. I didn’t want to put Q into ‘trust’ mode, choosing rather to review each change as it was prepared. I did this so I could interrupt Q when it went off the rails, and also to reduce the amount of generated code I needed to review. This meant a lot of time waiting while Q was ‘thinking’. One colleague talked about their passion for writing code and how reviewing generated things is not the same. In their current form, these tools don’t have the responsiveness of working directly with code.

The production of the code also produced a strange effect around ownership. Hand-writing code (or whatever we call the ‘old’ ways of programming) meant taking care with each method. It was a good way to get inside the code, producing ‘mechanical sympathy’. Here, I started with a simple outline of my application in 275 words. Q produced over 10,000 words of feature files (including some useful functionality that was not asked for, such as sanitising inputs). This is a lot of reading! Assuming a reading rate of 400 words per minute, that is 25 minute’s work – setting aside the deeper understanding needed here, and any editing required.

Q also proved to be better at some things than others. When asked to generate some test data, Q created a programme to populate the DB on start-up. I had to suggest using liquibase. Being able to get the best out of this tool requires the operator to have a clear idea of what they would expect.

I’m still convinced that these tools will be part of a regular toolkit, but I don’t think they will offer the sort of incredible gains some have suggested – although they will be essential for prototyping. Cal Newport produced a great summary of the competing claims about productivity. My prediction is that, in the long run, we’ll see significant gains, but we won’t be relying solely on the agents.

My employer has organised a ‘Summer of Q’, where a number of us have signed up to play with AmazonQ. This weekend was the first time I could work with Q in depth. The main result – I ‘built’ a quiz application in 30 minutes (while also doing some chores) and it looked and worked better than what I’d have produced solo. But there are a lot of subtleties and caveats to add to this.

• A major argument against GenAI putting developers out of work is how poor the tooling and signup flows for Q are. The signup is terrible and confuses a lot of people. Q failed to help, and kept hallucinating links to help pages that didn’t exist. The IntelliJ plugin is awful and locks the IDE, so I’ve had to use the command-line version instead.
• Q is great at producing code. Producing the quiz example was a trivial task, so I’m now working on a much more complicated example. Straight away, I can see Q making me more effective. Personal tools I’ve wanted to make, that I decided against investing time in, now look easy.
• The quiz app that Q produced looked and played better than what I could have produced by myself. I’m very impressed by this.
• The model’s reasoning is clever and spooky – it makes mistakes sometimes, but then works to fix those. Interesting behaviour – although I expect there to be fewer mistakes in the generated code over time.
• One of the challenges of coding agents is getting used to the new workflow. There’s a fair bit of waiting involved while Q thinks about each file that needs creating. It’s very different to using a GenAI coding assistant, and I need to figure out the best new workflow.
• An ongoing problem with GenAI is that it involves a lot more reading than writing. I figure almost no-one is reading co-pilot meeting summaries, and I worry that not everyone will closely read the impressive amount of code that Q generates.
• At present, I’m reviewing each action Q takes, rather than trusting it for the session. It’s going to be interesting to how other people are working. There’s a lot of boring waiting this way, but a lot less reading to do in one go.
• Being able to produce decent (albeit not perfect) code so quickly will change the nature of programming. The coding part is going to get much easier. The development part – making sure the right thing is produced – will become more important, and maybe more difficult. I’m currently using feature tests as a way of validating what is being made.
• Something I’ve noticed with GenAI in a number of areas is the importance of taste. The tools produce things (image/text/code) incredibly fast, and require an operator with strong opinions about this output.
• Q responded to my initial, naive prompts by producing ornate additional features. For example I asked it to generate some BDD feature files and it’s adding some complicated accessibility tests. I’m looking forward to watching it try to fill those out! I also spotted some subtle divergences from the spec that I need to edit. The quiz code I initially generated also included a lot of useful but unasked-for features. They were improvements, for sure, but it was definitely not an MVP. It will be interesting to see how easy it is to work with Q on my more complicated application.

From Ashley Willis:

“[A mentor] pointed out that debugging AI generated code is a lot like onboarding into a legacy codebase, making sense of decisions you didn’t make, finding where things break, and learning to trust (or rewrite) what’s already there. That’s the kind of work a lot of developers end up doing anyway”

From Sean Goedecke:

Being good at debugging is more useful than being good at writing code – you only write a piece of code once, but you may end up debugging it hundreds of times¹. As programmers use more AI-written code, debugging may end up being the only remaining programming skill.

Continuing my research on RAG (part 1 here)

The week before last I worked on a simple example of RAG using Spring. This involved a lot of yak shaving, in part because spring had updated their package structures since I last worked on my RAG demo. I also wanted to set up a local vector DB without using docker, finally settling on MariaDB. In the end I had a simple example that took a CSV, inserted the rows as embeddings into a vector database and could run simple queries against it. I still need to tidy this up and upload it to github.

Interesting Links

• I need to look into using vector databases for recommendation engines.
• “If you want to make a good RAG tool that uses your documentation, you should start by making a search engine over those documents that would be good enough for a human to use themselves.” – link
• Simon Willison gave a great talk about embeddings. This introduced the concept, showed how to do ‘vibes-based search’ against sqllite using llm, and talked about 2D visualisations.
  • “Being able to spin up this kind of ultra-specific search engine in a few hours is exactly the kind of trick that excites me about having embeddings as a tool in my toolbox.”
  • “A fascinating thing about RAG is that it has so many different knobs that you can tweak. You can try different distance functions, different embedding models, different prompting strategies and different LLMs. There’s a lot of scope for experimentation here.”
• Adding semantic search to datasette
• Lovely visualisation of 40 million Hacker news posts. This uses Uniform Manifold Approximation and Projection (UMAP) to reduce the vectors to a 2D geographic map (among other things). Some interesting applications of a massive dataset.
• The llm tool can be used for image search using CLIP
• This leads to someone searching images of faucets by image and phrase (what taps best represent the idea of ‘Bond villain?’)
• According to ChatGPT, people have played with using vector databases to analyse recipes. There is a paper on this, ‘Learning Cross-modal Embeddings for Cooking Recipes and Food Images‘, but I can’t find any details on applications with it, experimental or otherwise.
• Interesting discussion of search in RAG (look for the RAG section)
• Text Embedding Models Contain Bias. Here’s Why That Matters – interesting Google paper from 2018
• Using a vector database, SkyCLIP, and Leaflet to create a searchable aerial photograph
• “This is why Retrieval-Augmented Generation (RAG) is not going anywhere. RAG is basically the practice of telling the LLM what it needs to know and then immediately asking it for that information back in condensed form. LLMs are great at it, which is why RAG is so popular.” link
• Spring AI documentation on RAG

I’ve been thinking recently about Resource Augmented Generation (RAG) and vector databases, and I wanted to gather some notes towards a talk.

The question

Most of the applications I’m seeing for Generative AI seem to involve RAG. But I feel that the vector database is doing most of interesting work here – and, in a lot of cases, an LLM-generated response is not always the best ‘view’ for the data that is returned. I want to dig a little more into vector databases, how they work, and what can be done with them.

Panda Smith wrote, “If you want to make a good RAG tool that uses your documentation, you should start by making a search engine over those documents that would be good enough for a human to use themselves”; I want to learn more about this search part of RAG

Previous posts

• Playing with embeddings was a post I wrote in September ’24 looking at using vector databases for ‘vibes-based search’
• GenAI is already useful for historians discussed an article about a historian using GenAI to find diary entries relevant to their research
• Retrieval-augmented generation using SpringAI was a Spring AI RAG demo I built for a previous talk

Notes from wikipedia

Pulling out some notes from the wikipedia article:

• “[RAG] modifies interactions with a large language model so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information.”
• There are two phases – information retreival and response generation
• RAG was first proposed in the paper ‘Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks’ in 2020
• The process involves the following stages:
  • Indexing – the documents to be searched are stored – usually by converting the data into vector representations
  • Retrieval – given a user query, a document retriever finds the most relevant documents for the query
  • Augmentation – the documents are put in a prompt for an LLM
  • Generation – the LLM generates a response based upon the prompt
• The ‘chunking’ of the documents (how they are divided up into pieces to be stored) affects how good the responses are.
• Risks of RAG
  • While RAG reduces hallucinations in the responses, it cannot eliminate them.
  • There is a danger of losing important context in the chunking phase.

Interesting links

• Twitter thread by Jo Kristian Bergum on ‘The rise and fall of the vector database infrastructure category’
• The Best Way to Use Text Embeddings Portably is With Parquet and Polars – fascinating discussion of vector databases, using Magic: The Gathering cards as a dataset
• Embeddings: What they are and why they matter Text of a talk by Simon Willison: “Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon”