The situation
A provider-model change alters how an assistant answers important questions.
Production LLMOps
LLMOps manages changes and operations for language-model applications. Prompts, model choices, retrieval configuration and tool permissions can all affect behavior and need a reviewable release path.
- Version the behavior
- Create meaningful gates
- Observe and improve
Is this right for you?
When this is the right work.
A generative AI prototype is becoming a production dependency, but nobody can confidently compare releases or explain quality and inference costs.
The first conversation establishes the environment, the constraint and who will accept the result. We agree a bounded starting point before expanding the engagement.
Scope
What DotConn delivers.
- 01
Version the behavior
Record prompts, model settings, retrieval and relevant tool configuration together.
- 02
Create meaningful gates
Evaluate representative tasks, groundedness, unsafe or unsupported actions and expected abstention.
- 03
Observe and improve
Monitor agreed quality signals, latency and inference or GPU costs; define escalation and rollback.
Deliverables
What you receive.
Acceptance criteria identify the checks, records and handover needed for the agreed scope.
- Versioned application behavior and release policy
- Evaluation suite and promotion gates
- Quality, cost and failure monitoring guidance
In practice
A practical example.
Our approach
We compare the change against a maintained evaluation set before promotion and document the fallback path.
The useful result
The team can make a reviewable release decision rather than relying on an impressive demonstration.
Responsibility
Agree the responsibility.
DotConn
Assesses and delivers the technical work in the agreed scope, with reviewable changes and handover. Continuing operation requires a separately defined managed-service scope.
Your team
Provides authorized access, business priorities and accountable decision-makers; approves policy choices and accepts the agreed result.
Set this boundary early
No evaluation suite guarantees every answer. Human decision boundaries and data-handling rules remain explicit.
FAQ
Questions worth resolving.
Can we guarantee that the model never hallucinates?
No. We can engineer grounding, refusal, evaluation and human review appropriate to the risk, while being clear about residual uncertainty.
Pricing, timing and how to prepare are covered in working with DotConn.
Connected work
Services that work alongside this.
Retrieval-Augmented Generation
RAG retrieves relevant material and supplies it as context to a language model. A useful system must evaluate both the retrieval step and the answer produced from it.
ExploreAgentic AI Workflows
An agentic workflow uses a model to help coordinate a sequence of tasks and tools. Production design must define what the workflow can access, which actions it may take and when a person must decide.
ExploreProduction MLOps
MLOps engineers the lifecycle of machine-learning models: data and model validation, versioned deployment, monitoring and controlled change. It connects a model experiment to a maintainable production service.
Explore