EPS.
PREPARING THE EXPERIENCE
PREPARING THE EXPERIENCE
WEB / EXPERIENCE / SEARCH
Small, testable automations and AI-assisted workflows that fit an actual process, with attention to validation, privacy and maintainability.
THE OBJECTIVE
AI is most useful when it addresses a specific task and works within a clear software system. Python can help process files, validate data, generate reports, connect APIs and orchestrate repeatable workflows. Language models can assist with summarisation, extraction, drafting or retrieval, but generated output remains uncertain and must be checked for the task at hand.
I focus on practical learning and bounded implementations rather than suggesting that an AI agent should run everything. A useful scope defines the inputs, acceptable outputs, exceptions, human review points, data permissions and a way to measure success. Where a simpler script or existing software works, it may be a better choice than adding a model or framework.
Small automations can organise files, validate spreadsheet exports, combine reports, transform structured data or check lists against documented rules. They should keep source files safe, log what happened and fail clearly when assumptions break. A script that produces a plausible but incorrect output is more dangerous than one that stops and asks for review. A pilot should use sample data, record baseline effort and define what counts as a successful run before it is put into a recurring workflow.
A web product can connect to a model through a server-side API and present results in a clear interface. The engineering includes request validation, authentication, rate limits, timeouts, error states, cost controls and output checks—not just the prompt. Keep provider credentials on the server. Explain what the feature can and cannot do, and give users a way to review, correct or reject output when it matters. A narrow feature with clear boundaries is easier to test and maintain than a vague general-purpose assistant.
When an application needs to answer from a specific collection of documents, retrieval can fetch relevant passages to provide context to a model. The system needs to preserve source references, apply document permissions and handle questions that the collection cannot answer. Chunking, embeddings, search thresholds and document freshness all affect the result. Start with a small, approved corpus and evaluate representative questions before expanding. A retrieval workflow should not be presented as accurate merely because it returns fluent text or displays a source link.
Before processing real business data, review what leaves the organisation, which provider receives it, how it may be retained and which permissions apply. Do not put secrets in source control or send private records to an unapproved service. Require confirmation for consequential actions such as sending an external message or changing an account record. Define log retention, failure handling and a way to disable the automation. These controls are part of the product requirements and should be considered before deployment, not added after a workflow becomes depended on.
Test both normal and difficult cases. Compare expected outcomes to the actual output, track failure rates and review the cost and latency of connected services. For AI output, include examples where the correct answer is to say the information is unavailable. For deterministic scripts, verify data integrity and rollback behaviour. Start with a single process, measure whether it truly reduces effort and only then consider scheduling, dashboards or broader agent behaviour. The aim is a dependable tool that a team can understand and maintain.
COMMON QUESTIONS
No. A conventional Python script or existing integration is often more predictable for explicit rules, calculations and repetitive transformations. Add AI only when flexible interpretation or generation helps.
Yes, a model API can be connected through a server-side endpoint with validation, credentials kept private, limits and clear failure states. The specific design depends on the use case and data sensitivity.
No. Model output can be inaccurate. Consequential actions should have validation, auditability and a human confirmation or review step appropriate to the risk.
A defined process, sample inputs, expected outputs, permissions, testing criteria, an owner, monitoring and a plan for maintenance or disabling the workflow.
LET’S DEFINE THE SCOPE
Share your goal, audience, existing site and the requirements you already know.