Language models inside a product
A language model can support tasks such as drafting, summarizing or answering questions about supplied material. Define the output the user actually needs and how it fits into the existing workflow.
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An AI feature needs a clear job. It might help someone study source material, interpret an image or work through information that would take time to review manually. A model alone is only one part of that feature.
Spacewalk connects AI models to products and data. The work includes the surrounding interface and application behavior: what information goes in, how the result is shown and what the user can do when an answer is incomplete or wrong.
What the work can include
Working with your information
For features that use documents or records, establish which sources may be used and who can access them. Retrieval, permissions and references should be considered before adding a conversational interface.
Reliability, cost and user control
Model responses can vary. Plan representative examples, error handling, usage limits and a way to review the result. Sensitive decisions may need human oversight; an AI-generated answer should not imply certainty the system cannot support.
Start with examples, not a model name
Bring a few examples of the input and the result you would consider useful. Describe who will use the feature, how often they will use it and what a failed answer would mean for them.
Include the data sources, any sensitive information and the systems the feature needs to connect to. Budget, response time and model-provider restrictions can shape the implementation. A small, defined task gives the project a clearer basis for evaluation than a broad request to add AI everywhere.
Questions before you start
Do we need to train our own model?
Not necessarily. An existing model, suitable instructions and access to relevant information may cover the task. Evaluate those options against real examples before assuming that model training is required.
How should we decide whether the feature works?
Use representative inputs and define what a useful response looks like. Assess incorrect or incomplete answers as well as successful ones, together with response time, cost and the user's ability to check the result.
Can we use confidential data?
That requires a review of data permissions, retention, provider terms and the chosen architecture. Share the constraints in the initial discussion; do not send confidential records in an unsolicited project email.