Prompts cannot hold every exception
Specialized corrections crowd the system prompt and can weaken behavior that already works.
Great AI products start from great data
Our approach / 01
We build real-world, use-case-specific golden datasets for AI products that cannot afford generic outputs.
RAHF turns expert corrections from production traces into approved experiences your agent can retrieve on future requests. The underlying model stays unchanged.
Why it matters / 02
Prompt engineering eventually plateaus.
A longer system prompt may fix one case and break another. Evaluations can flag failure, but they rarely give the agent a better example to follow next time.
Evaluations stop at diagnosis
A score can show that a response failed without capturing the response or trajectory that should replace it.
Expert corrections disappear
Your best fixes get buried in support tickets, internal reviews, edited responses, and team conversations.
Raw traces lack the chosen answer
Production history shows what happened, but rarely records approved behavior the agent can reuse.
What we build / 03
A reusable experience library for your agent.
We turn real production failures into approved responses and trajectories, then test whether retrieving them improves future requests.
Production trace reconstruction
We ingest OpenTelemetry traces or exports and reconstruct requests, model calls, tool use, intermediate steps, and final responses.
Human-reviewed corrections
Reviewers and domain experts rewrite responses, correct tool calls, clarify intent, or produce an approved trajectory.
An approved experience library
Every correction keeps its context, ideal response, critique, policy scope, version, and approval status.
One grounded record / 04
A correction the agent can use again.
Each approved experience keeps the original context, failed output, chosen rewrite, and the reason the correction is better in one traceable unit.
User trigger
“Great, I bought the ticket for tomorrow morning.”
Original AI output
“Your ticket has been purchased.”
Approved human rewrite
“You’re all set. Your ticket is booked for tomorrow morning. Enjoy your flight, and if any questions come up before you travel, come back anytime. We’re here to help.”
How RAHF works / 05
From production trace to a better next response.
The loop stays outside model weights, so every experience remains inspectable, scoped, versioned, and removable.
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01
Connect traces
Send OpenTelemetry traces or exports from the observability stack you already use.
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02
Review failures
Reviewers identify what went wrong and create the response, tool call, or trajectory that should replace it.
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03
Approve experiences
Your team controls the rubric, policy scope, metadata, version, and approval status.
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04
Retrieve at runtime
Relevant approved experiences enter the prompt as dynamic few-shot examples for similar requests.
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05
Measure the outcome
Compare task success, acceptance, escalation, editing time, latency, and cost against the existing agent.
Start with one workflow and a measured result.
The Agent Improvement Sprint tests retrieval-augmented human feedback before you change production architecture or commit to an ongoing program.
Audit the workflow
Production trace audit, failure and opportunity analysis, and an annotation rubric tied to your definition of success.
Build the experience layer
A human-corrected response or trajectory dataset plus a retrieval prototype for one repeated production flow.
Prove what changed
Baseline comparison, blinded quality review, cost and latency analysis, and a rollout recommendation.
You leave with a corrected experience dataset and measured evidence, even if you decide not to continue.
Related systems show the upside. Your workflow still has to prove it.
24% more correct responses and 35% more tasks completed.
Rounded relative improvements reported by related systems on specific tasks. These are not results achieved by RAHF Agent.
read the sourceImprove behavior without another model to maintain.
Fine-tuning can work, but it slows the correction cycle and makes individual examples harder to inspect, scope, replace, or remove.
Immediate correctionsNo training run is required before a reviewed example can take effect.
Identifiable sourcesEvery experience points back to its trace, reviewer, version, and approval.
Precise scopeFilter experiences by customer, workflow, policy, confidence, or date.
Safe removalStale or harmful examples can be disabled without retraining the model.
Independent modelsChange the underlying model while keeping the approved experience layer.
Complementary strategyUse weights for stable behavior and retrieval for specialized or changing cases.
RAHF works best when agent mistakes repeat.
The strongest starting point is a production workflow where humans already spend time reviewing, editing, or escalating agent output.
Strong starting signal
- Production traffic and repeated request patterns
- Existing traces or conversation histories
- Human review or editing already happens
- Errors and escalations are expensive
- Success can be measured in the workflow
Probably not the first move
- One-off creative tasks with little repetition
- No production traffic or usable trace history
- Nobody can define an acceptable result
- Corrections cannot be reviewed or approved
- There is no outcome to compare against baseline
Common applications include customer support, claims, compliance review, sales operations, document drafting, internal service agents, and tool-using workflows.
From measured sprint to managed improvement loop.
If the sprint produces measurable lift, we can help operate the correction, retrieval, and evaluation system in production.
Managed correction operations
Route selected traces to trained reviewers, maintain annotation quality, and keep the approved library current.
Runtime memory integration
Tenant isolation, metadata filters, confidence thresholds, and fallbacks inside your existing agent stack.
Continuous agent evaluation
Track which experiences help, which hurt, where coverage is missing, and whether gains survive model changes.
Internal team enablement
Annotation guidelines, reviewer training, retrieval evaluation, and operating procedures for your internal team.
Questions before the first trace review.
What is retrieval-augmented human feedback?
RAHF stores approved human corrections outside the model. When a similar request arrives, relevant corrections enter the runtime context as examples.
Does RAHF train or fine-tune our model?
No. The approved experiences stay outside model weights and are retrieved at runtime. Fine-tuning can still complement the system for stable, common behavior.
Is this the same as retrieval-augmented generation?
No. Conventional RAG usually retrieves documents or facts. RAHF retrieves examples of how an agent should handle similar situations.
Is this an AI agent annotation platform?
Annotation is one part of the workflow. The outcome is an approved experience library, measured retrieval behavior, and a production rollout plan.
Do we need OpenTelemetry?
It is the preferred ingestion format, but a first sprint can also use exports from an existing tracing, observability, or conversation system.
Who writes and approves the corrections?
We can work with internal experts, trained reviewers, or both. Your team controls the rubric, policy scope, and final approval criteria.
Can retrieved examples make the agent worse?
Yes. Irrelevant, stale, or incorrect examples can reduce performance. We use approval gates, metadata filters, retrieval thresholds, and controlled evaluation before rollout.
How quickly will our agent improve?
We do not promise improvement before examining the workflow. The first engagement determines whether the method produces measurable lift for your agent.
Start with evidence
Find out whether your existing corrections can improve your agent.
Send a sample of production traces. We will identify repeated failure patterns, estimate experience coverage, and recommend one focused test.
book an audit