From the AI Native Dev: Monthly Roundup: AI Tool Effectiveness, Context, Fin, and AI Autonomy.

Introduction

In this month's episode of the AI Native Dev podcast, hosts Simon Maple and Guy Podjarny do a monthly roundup to discuss various themes and insights from recent podcast episodes. This roundup gives you an opportunity to revisit key learnings, explore new themes, and understand the evolving landscape of AI tools, especially in the context of software development.

Evaluating AI Tools: Promises vs. Reality

The hosts delve into the challenges and realities of evaluating AI tools. Simon and Guy discuss the difficulty in assessing whether AI models work as expected. Guy explains, "It's very hard to know whether these products work. And it's interesting how consistent that was."

They emphasize the importance of torture tests in evaluating AI tools. Guy mentions, "Des said it best when he described the difference between their sort of good case and what they call the torture test. Everyone should have a set of torture tests in their test suite."

Guy also highlights a key challenge, "You don't know if your product works until you ship it. And that's super, super hard." Simon adds, "The evaluation we covered a lot. And of course, Rishabh, is the head of AI and lives in that ML space and so much of his work is really about that evaluation."

The Importance of Context in AI Code Generation

Simon and Guy emphasize the significance of context when it comes to AI code generation. Guy explains, "When you're trying to mimic intelligence, it's very hard to know whether you had an intelligent answer or not."

Simon shares insights from their conversations with Peter from Tabnine and Des from Intercom. He states, "Providing the right amount of context will allow it to actually be far more accurate in providing that answer."

Guy recalls a quote from Des, "If your code is full of bad practices that you actually don't want to maintain, then lo and behold, if you train on your code, you might be replicating practices that are not really what you desire."

Simon adds, "In code completion, you would expect an answer very quickly. However, if it was something beyond that and you're actually getting more back, maybe it's a test generation or something like that, it allows the LLM to actually have far more time to generate that answer."

The Consumer Perspective: Trust and Reliability

This section explores the consumer's perspective on AI tools, particularly focusing on trust and reliability. Simon and Guy discuss how consumers experience the same pain points as developers when using AI tools. Guy notes, "It's frustrating because we want to lean in, we want to be early adopters, we're believers in the destination."

Simon shares, "In some areas, they've been great time savers. In others, they give me an answer, but it's never the right answer." He emphasizes the importance of revisiting these tools regularly as they evolve. "Revisiting, making sure that our assumptions of where we were six months ago are actually still true and where we can actually improve where we are today," Simon adds.

Categories and Scope of AI Development Tools

Guy and Simon talk about the different categories of AI development tools, as described by Amir. Simon states, "There are really nice categories that we talked about of, it's great for a tool to exist in this AI code assistant, as well as, a documentation tool would be great, as well as a testing tool."

They highlight the overlap between these categories in practice, as observed in their discussions with various vendors. Guy explains, "A lot of these vendors are just trying to seize a broader scope. And I think time will tell, I don't know exactly where the lines would be drawn."

Simon adds, "If I was a developer that wanted to do all of these things anyway, and I will want to do them, whether it's testing, whether it's documentation, whether it's the code completion, the AI assistance, that help there, would I want three tools, or would I just want one consolidated tool?"

Handling Hallucinations in AI Responses

The hosts address the issue of hallucinations in AI-generated responses. Guy discusses the balance between creativity and accuracy. "It's very clear that if you allowed Fin to hallucinate more, you can resolve more cases," he states. "The problem is that how do you get more creative, without losing trustworthiness."

Simon shares an interesting discussion with Devin from Dosu. "LLMs don't like answering, I don't know. You have to really be clear in the training with them that there needs to be some limit or some bar whereby if they don't have a level of confidence that is above that bar, it should say, I don't know."

Guy adds, "In their maturity journey, I didn't see it in action, but as I was commenting that Fin has an L, it tells you how confident it is, in its answer."

The Role of Data in Enhancing AI Tools

Simon and Guy emphasize the critical role of data in improving the performance of AI tools. Simon explains, "Data is going to be king here. In terms of the data of understanding behavior of how things are using the application."

He provides examples, "If I have an AI tool, which was a monitoring tool, for example, can take production data about where code paths, which code paths are the hot code paths. Perhaps you've got behavioral data that show which areas of the code base are being changed most by developers."

Guy adds, "Data is one aspect. Do I already have data that is valuable and therefore I can do something better? Sometimes it's just distribution. It's just, okay, I'm already installed on everybody's desktops."

Simon concludes, "All of this data can really help us in terms of the other tools recognize where they should focus on more."

Upcoming Changes and New Formats

The hosts announce upcoming changes to the podcast format, including the introduction of hands-on sessions that will be more dev-focused and better suited for video. Simon explains, "My sessions are going to be changing just slightly, whereby I'm going to be releasing two sessions per guest, back to back on the same day."

They also mention some exciting upcoming episodes. Guy shares, "I will mention, Itamar, I've already alluded to that. Itamar, the co-founder and CEO of Codium AI. We'll talk about AI testing and more."

Guy concludes, "We would welcome feedback. We're still at contact@tessl.io. If you have thoughts about how you want us to change that."

Summary

In this month's roundup, Simon and Guy covered a range of insightful topics:

• The importance of context in AI code generation.
• Challenges in evaluating AI tools and ensuring their reliability.
• The evolving categories and scope of AI development tools.
• Managing hallucinations in AI responses.
• The critical role of data in enhancing AI tools.
• Upcoming changes to the podcast format and exciting future episodes.

Listeners are encouraged to stay tuned for more insightful discussions and hands-on sessions in the coming months. The hosts invite feedback to continuously improve the podcast and make it more valuable for their audience.