Launching today
Linden
Validate AI Outputs Before They Reach Your Application
14 followers
Validate AI Outputs Before They Reach Your Application
14 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.
love the four-tier decision system, that's genuinely useful. one thing i'd want as a user is a way to set custom actions per rule, like routing WARN outputs to a quarantine queue or auto-fixing common issues before they hit REGENERATE, basically making the remediation step part of the config.
@cemreozome60888 Thanks, Cemre! Really appreciate the feedback. The goal with the four-tier decision system was to make AI failures more actionable instead of just returning errors.
I really like the idea of making remediation part of the configuration — things like routing certain WARN cases to a review queue, applying automatic fixes for known issues, or defining custom actions per rule could make the workflow much more flexible.
I’m curious, how are you currently handling these kinds of failures in your AI pipelines? Do you already have workflows like quarantine/review queues, or is this something you would want Linden to manage end-to-end?
the warn and regenerate actions are honestly super useful, not just a pass fail thing. tested it on some messy outputs from claude and the clear validation decisions saved me from a bunch of weird edge cases.
@ksevim90613 Thanks, Sevim! That’s exactly the reason we built the decision model — AI reliability usually isn’t just a simple pass/fail problem. Having a middle ground like WARN and a recovery path like REGENERATE can help teams handle imperfect outputs without immediately breaking the workflow.
I’m curious, what kind of use case were you testing with Claude — agents, structured extraction, or something else? And if Linden became part of your workflow, what would you need it to support to make it valuable enough for regular use?
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?