Objective & prerequisites
Build a small evidence report that makes model, parser and reviewer limitations visible. Prerequisites: Python functions, JSON, lists and the existing API/testing foundations. Extract the full kit and open a terminal in applied-ai-kit. The core lab uses Python 3.10+ with no third-party dependencies or network. Actual new-text semantic inference is a separate optional local setup, tested on Python 3.12/macOS arm64. All CedarLab policies, invoices and review cases are fictional teaching material; they are not DataCareerHub service terms. No payment, registration, live chatbot or visitor-data collection is activated. These free companions do not change course progress or protected access.
python3 lab.py
python3 -m unittest discover -s tests -vUnderstand the concept
A useful portfolio artifact lets a reviewer reproduce its claims. Keep the corpus, version/eligibility rules, queries and relevance labels, model identity, extraction schema, review rubric and failure cases together. Separate a retrieval scorecard from extraction agreement and factual-support judgments. Preserve the original fixtures and add a clearly labelled new scenario rather than editing expectations until the code passes. If you add a passage or query, its embeddings must be regenerated with the pinned model; the saved-vector mode deliberately detects stale source hashes. A new real deployment is outside this exercise and requires its own security, privacy and operating review.
Worked example
The baseline artifact includes keyword versus semantic rankings, six narrow invoice outcomes and eight human-reviewed claims. Its visual report shows both successes and deliberate failures. A good extension adds one invented paraphrase, one ambiguous extraction and one claim with a changed number. Establish expected relevance/handling before executing, run actual local encoding for new text if available and mark any skipped optional runtime honestly. A reviewer can then inspect your own contribution without confusing it with supplied teaching code.
python3 lab.py
python3 -m unittest discover -s tests -v
# Optional actual new-text inference after explicit setup:
# python3 encode.py --model-dir ./model --query "Can I hand my workshop credit to someone else?"Try it yourself
Complete the worksheet and mini-project rubric. Produce a 250–400 word evidence memo, one new case per boundary and test outputs. State whether you used real inference or only replay. If you cannot install the model, complete the core lessons and label the new semantic case unexecuted; do not invent a vector or relabel a keyword score as semantic. Write your prediction, inspect the source and record actual results. Keep a copy of the original fixtures before making changes.
Worked solution
A strong memo reports three-answerable-query test MRR with its tiny denominator, the unsupported Q7 case, exact extraction failures and the difference between citation membership and claim support. It identifies all fictional data, records model/runtime provenance and discloses that no generative extraction, autonomous support judge, production vector database or hosted adapter is delivered. No job offer, client outcome or production-readiness guarantee follows from completing the activity.
Knowledge check
Would a working local semantic search justify claiming a deployed, fully factual AI assistant?
Check and explain
No. It proves the bounded local encoder/retrieval work actually executed. Generation, truth evaluation, authorization and operating acceptance need separate evidence.
Evidence, mistakes & handover
Record the executed mode: actual local encoder, saved-vector replay, deterministic extraction or human factual review. They establish different things. Keep model/version, source hashes, commands, expected outcomes, failures and limitations with your work. Never turn a schema pass, cosine score or quotation into a claim of proven truth. The download contains a worksheet, independent controls, worked solutions, a factual-review rubric, privacy/cost contract and mini-project checklist. Credit the supplied starting point and describe your own contribution.
Authoritative reference
— official source for the underlying mechanism. Teaching prose and CedarLab cases are original. Model/runtime compatibility, licensing and operating limits require review for any later deployment; this course does not activate a production AI service.
Try the activity before reading its reference. Keep your sources, commands and assumptions with your work. Companion activities are unsaved self-practice and do not award a certificate or change lesson progress.