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Linden
Validate AI Outputs Before They Reach Your Application
12 followers
Validate AI Outputs Before They Reach Your Application
12 followers
LLMs can generate malformed JSON, missing fields, schema violations, and inconsistent outputs that break production applications. Linden is an AI reliability layer that validates structured AI responses before they reach your system. Define schemas and business rules, then receive clear validation decisions: ALLOW, WARN, REGENERATE, or BLOCK. Integrate in minutes using our API and Python SDK.
A REGENERATE action is solid, but it would be even more useful if you let teams define custom retry prompts per field, so the model knows exactly what to fix instead of getting a generic response back.
@aryaaltuntokiv Great suggestion, Arya! Right now, the idea is to generate a structured regeneration prompt using the validation errors and logs, then feed that back to the model for a limited number of retry attempts. The goal is to make each regeneration more targeted instead of just asking the model to try again.
I really like the idea of allowing teams to define custom retry prompts at the field level though — that would give developers much more control over how specific issues are corrected. Thanks for the thoughtful feedback!
Having used similar validation layers before, one thing I'd love here is custom rule chaining where I can define fallback logic if REGENERATE fails after N attempts. Right now I'm guessing it just blocks on persistent failures, which is fine, but being able to say "after 3 regenerations, fall back to WARN with the partial output" would make this way more production-ready for cases where getting any answer matters more than a perfect schema match.
@dilarakurtt8sl Thanks, Dilara! This is a really thoughtful suggestion. You’re right that in production systems, reliability isn’t always about achieving a perfect output — sometimes a usable partial answer with the right controls is better than repeatedly failing.
Right now, Linden treats REGENERATE as a recovery path with validation feedback, but configurable retry limits and fallback strategies would make the workflow much more flexible.
I’m curious, in your experience with similar validation layers, where do you usually need these fallback strategies most — AI agents, data extraction pipelines, or another type of workflow? And how do you currently handle cases where regeneration keeps failing?
Plugged it into a small pipeline and honestly the WARN versus BLOCK distinction saved me from a ton of silent failures. The Python SDK was basically drop-in, which is kind of rare for this kind of tool.
@ayhanargundogan Thanks, Ayhan! Really appreciate the feedback. The WARN vs BLOCK distinction is something we focused on because many AI failures aren't severe enough to block execution but still need attention.
I'm curious about the pipeline you tested it with — what kind of workflow were you validating (agents, extraction, automation, etc.)? I'd love to learn what types of failures you were seeing and what would make Linden more useful for your production use cases.
A streaming mode would be super helpful so we can validate tokens as they come in instead of waiting for the full response. Right now we still have that whole latency hit before knowing if the output is even usable, which defeats the point for real time agents.
@aylinyeennadi Thanks, Aylin! You’re absolutely right — with real-time agents, waiting for the full response before validating can add unnecessary latency and reduce the value of having a reliability layer in the first place.
Streaming validation is something we’re interested in exploring as Linden evolves, especially around detecting issues earlier while the response is still being generated.
I’m curious — what types of real-time agents are you working with today? And if Linden supported streaming validation, what would be the most important thing for you: catching invalid outputs earlier, reducing latency, or enabling automated recovery during the stream?
Love the ALLOW/WARN/REGENERATE/BLOCK decision model, super practical. One thing that would make this way more useful for us is built-in support for streaming responses, since most of our LLMs stream output and we currently have to buffer everything before validation kicks in. Even partial validation per token chunk would be a huge win.
@tlinzheb Thanks, Tülin! Really appreciate the feedback. Streaming validation is definitely an interesting challenge, especially as more AI applications move toward real-time agents and user-facing experiences.
Right now, Linden focuses on validating complete structured outputs, but validating partial chunks as they stream is a great direction for reducing latency and catching issues earlier.
I’m curious — what kind of streaming workflows are you building with LLMs today (chatbots, agents, extraction pipelines, something else)? And if Linden supported streaming validation, what would be the most important behavior for your use case?
The Python SDK made it super easy to plug into my existing pipeline, and getting back clear ALLOW or BLOCK decisions instead of just error logs is genuinely useful. Wish I'd had this a few months ago when I was debugging a nightmare of malformed outputs from a smaller model.
@mcahitm1z5 Thanks, Mücahit! This is exactly the problem we’re trying to address — AI failures are often not just about detecting errors, but giving teams clear decisions they can act on. Glad the ALLOW/BLOCK approach was useful for your workflow.
I’m curious, when you were dealing with those malformed outputs, was this something you were handling regularly in production or more of a one-off debugging challenge? If you were using a reliability layer like Linden long term, what features would make it valuable enough to integrate into your stack?