Savyre AI Coding Workflow

Great question Evie. Tech leads don’t need to start with a blank rubric. Savyre provides a structured evaluation framework covering areas like requirement understanding, code quality, testing, review quality and AI over-reliance. The goal is to make AI-assisted coding quality measurable and consistent across developers, while still allowing teams to adapt the criteria to their own standards.

Thanks @Ivan. We provide ready-to-use, role-specific assessments, so hiring managers don’t need to build everything from scratch. They can pick the relevant role and assessment and customize it based on their hiring needs, which makes setup fairly quick.

Thanks @Brandon. That’s exactly the idea. Since each stage builds on the approved output of the previous stages, the reasoning and context don’t disappear into chat history. When requirements change, developers can revisit the relevant decisions and understand what downstream work may need to change instead of starting from scratch.

Great question, @Oskar. Yes, Savyre tracks token usage at each stage, including estimated tokens usage and the approximate cost. This makes it easier to see where AI usage is highest and identify stages where context or prompts could be optimized.

Congrats on the launch! Requirements change all the time once a project gets going. That can cause a lot of extra work when the original plan no longer fits what the team needs. Keeping all the stages connected should make it easier to update the plan and keep the work on track without starting everything over. That's really useful for real projects. Looking forward to seeing how people use Savyre..

AI coding can burn through tokens pretty quickly when the same context gets repeated across prompts. Savyre's staged approach looks like it could make that easier to manage by keeping each step focused. Can we see where most of the usage goes or spot stages that are using more tokens than they should?

Thanks @Johan. That’s exactly why we wanted the trial to work on real repos rather than a controlled demo. Would love to hear what works, what feels unnecessary, and where the workflow gets in your way. That feedback is genuinely useful for us.

Exactly, Klara. We don’t want the reasoning behind a change to disappear into an AI chat. Keeping the requirements, design decisions, reviews and impact analysis as artifacts makes it much easier for someone to understand why something was done even months later.

@Zain Sheikh : Great question. Impact Analysis is meant to go beyond just the files that were changed. It uses the codebase context to look for dependencies and other areas that could be affected by the change, the goal is to catch unintended impact before the code is submitted.

Thanks @Viktor, and glad the stage prompts helped! Yes, making the workflow adaptable to team-specific coding, architecture and review guidelines is something we’re working towards. The idea is to keep the core structure consistent while letting teams bring in their own engineering standards.

Thanks @Natalie. The core Savyre workflow is the same across Cursor and VS Code - same stages, artifacts, checkpoints and traceability. The main difference is how you interact with the AI capabilities available in each IDE. We intentionally designed the workflow so teams aren’t locked into a particular AI coding tool.

Thanks @Anders. I’d actually start with small bug fixes or refactoring tasks. They’re easier to validate and let the team get comfortable with the workflow without adding too much process. Once it becomes natural, moving to larger features makes much more sense.

Savyre's hiring assessments skip the algorithmic trivia and focus on real-world debugging instead which is exactly how you actually test someone. How long does it usually take a hiring manager to set up one of these role-specific assessments?

We have no idea if our devs are actually getting better at prompting or just generating more garbage code. Savyre's Evaluator using a standardized rubric seems like the only way to actually measure AI coding quality. Do tech leads have to write the rubric from scratch or do you provide templates for things like "AI over-reliance"?

Everyone talks about Cursor but a lot of enterprise teams are locked into standard VS Code for compliance reasons. We use plain VS Code for a lot of our work so it's good to see Savyre work there too. Are there any feature differences between the Cursor and VS Code versions, or is it a 1:1 parity?

Tried Savyre AI Coding Workflow today to fix a small bug. The built-in stage prompts are actually a lifesaver because I usually just stare at a blank chat window wondering how to phrase things for the AI. Having a ready made prompt for the 'Understand' and 'Plan' stages kept me from just blindly pasting code into Cursor. That got me wondering, can we tweak those default stage prompts to match our team's specific PR and architecture guidelines?

The versioned artifact per stage is the interesting part here, since it makes an AI-written change reviewable instead of just faster to produce. Does the impact analysis stage look across the whole repo or only the files touched?

I noticed Savyre explicitly mentions the workflow extension is for internal practice and not for hiring assessments. That’s honestly a big relief. Many platforms blur the line between helping developers learn and secretly grading them. Having a dedicated, private space just to build good AI habits without the pressure of being evaluated is exactly what teams need to actually get comfortable with these tools. Question for you: do you recommend teams start by using this on brand-new features to build those habits, or is it better to just run it on refactoring tasks first?

When our team has to revisit an architecture decision months later, digging through old chats and project notes can take longer than it should. Savyre AI Coding Workflow's HTML and PDF reports could be really useful here. We could share them with a tech lead or keep them with the project so the reasoning behind those decisions doesn't get lost. That could save us a lot of time when the project changes or new developers join the team. PS: The 14-day trial is a smart way to let people test it on a real project instead of a tiny demo.

@Savyre: Sandbox demos rarely tell you if a new tool will actually survive the chaos of a real sprint. Having a 14-day trial where we can actually test it against our own repos is the only real way to see if the structure pays off. Definitely taking this for a spin on my current repo. Best of luck with the launch!

@mattias_blomqvist : Thanks for highlighting that. Security and developer trust were major design goals for Savyre. Keeping repositories local while providing a structured AI workflow lets teams adopt AI without compromising control over their source code.

@Freya_Jensen : Thanks, that's a great suggestion. Since every stage is stored as an artifact, you can revisit earlier decisions without relying on chat history. We're also exploring making navigation across previous stages even smoother while keeping the full decision trail intact.

@Kassidie : Thanks! We faced the same issues within our dev team. Built Savyre to address this issue. Savyre preserves context throughout the workflow, so requirements, codebase discovery, design decisions and reviews stay connected. That means less time rebuilding context every time you switch tasks or revisit a feature. Do try it and help to provide valuable feedback.

@FinleyCarter_363 : That's exactly one of the problems we built Savyre to solve. Instead of relying on individual review habits, every developer follows the same structured review workflow with stage-specific artifacts and checkpoints, making AI-assisted development much more consistent across teams.

@AidenPearce_1ef : Every stage in Savyre produces a versioned artifact that becomes the input to the next stage. If a test fails, you can trace it back through the test plan, implementation, design and even the original requirements to identify exactly where the decision originated, instead of debugging only the final code.

Security is usually the biggest roadblock for teams trying to adopt AI coding tools. Most extensions want to send your whole repo to their cloud, which is a non-starter for proprietary code. The local-first architecture here completely bypasses that headache. You get the strict workflow, but your code never actually leaves your workspace.

When a test fails late in the workflow how does Savyre handle tracing the issue back to its root? Is there a way to see which earlier decision or requirement might have led to the failure?

AI has definitely made coding faster for our team but code reviews are still inconsistent because everyone evaluates AI-generated code a little differently. Savyre AI seems like it could help by giving teams a structured review process instead of relying on individual habits. Once multiple developers are involved, keeping reviews consistent usually becomes a bigger challenge than generating the code itself.

Most of my AI coding time goes into checking requirements, reviewing generated code and figuring out what changed before I can move forward. A structured workflow like Savyre could cut down that back and forth, especially when switching between tasks during the day. The codebase discovery and review stages stood out to me because they are usually the first places where context gets lost. Nice to see those getting as much attention as the code generation itself. BTW Congrats on the Launch!

I like that this treats AI coding as a workflow instead of just another chatbot. Having requirements, planning, testing and reviews connected together feels a lot more practical for real projects. One thing I'd add is a way to jump back through previous stages without losing context. That would make it easier to revisit earlier decisions after code reviews or changing requirements. Building trust around AI-generated code is a harder problem than generating it, so I like the direction you're taking.

@Tahir. Thanks for the question. Yes, all stage context lives in the stage files. If the developer switches AI agents, the next agent still has full context by reading those files (ai-output.md, final.md, review, etc.). Nothing depends on a single agent’s chat history.

A lil question came to my mind. How does Savyre handle projects where developers jump between Claude, Cursor and GitHub Copilot during the same workflow? Does it keep the context and checkpoints consistent across those tools?

What I like most about Savyre AI Workflow is that it adds a clear process to working with AI . Rather than relying on back-and-forth prompts, everything stays organized from understanding the task to reviewing the final changes. It makes using AI feel much more predictable and productive.

I’ve been using Savyre AI Workflow in VS Code, and it’s changed how I work with AI coding assistants. Instead of jumping straight into coding, it guides me through understanding requirements, exploring the codebase, planning, implementation, testing, and review. The structured workflow keeps AI-generated code organized, traceable, and much easier to trust. It feels like having an engineering playbook built directly into the IDE.

Hi DevHunt community, We built Savyre AI Coding Workflow because AI can generate code quickly, but teams still need clarity around requirements, codebase understanding, testing, review and ownership. Savyre guides developers through 14 connected, reviewable stages inside VS Code and Cursor. Each validated output becomes the input for the next stage, helping preserve context and improve traceability. We’d love honest feedback from developers and engineering teams: **Which part of AI-assisted coding is hardest for you to review or trust today?** A 14-day free trial is available for anyone who would like to test it on a real or sample project. Demo URL: https://www.youtube.com/watch?v=-StqPB_TnwI Visit us at: https://savyre.com/products/ai-coding-workflow