Topic
data
AI news and tutorials tagged “data”.
How to measure time and quality in an AI workflow
Measure the complete AI workflow: include preparation, generation, review and corrections, define observable quality and compare cases without promised gains.
How to record AI automation executions and errors
Plan logs for AI automations: connect stages by execution, distinguish failure from waiting and record useful context without exposing credentials.
How to define human approval in an AI automation
Define human approval before action: show content and destination, identify the decision owner and handle denial, revisions and proposal changes.
How to validate AI-generated JSON before using it
Validate AI-generated JSON in three layers: syntax, schema and system rules. Test fields, types and values before permitting an automation action.
How to design an AI automation: triggers and actions
Design an AI workflow separating triggers, validation and actions. Define inputs, exceptions and repeated-event handling before configuring integrations.
How to check AI-suggested libraries before installation
Check Python dependencies suggested by AI: verify names, documentation, compatibility and necessity before planning an installation test.
How to use AI to document an API from its contract
Document an API with AI without inventing features: verify methods, paths, parameters, responses and gaps against the supplied contract version.
How to refactor a function with AI without changing behavior
Refactor a function with AI while preserving its contract: define inputs and outputs, create boundary cases and compare versions before accepting changes.
How to use AI to understand a Python error
Understand Python errors with AI: preserve the traceback, build a minimal example and test a small correction without hiding failures or changing the rule.
How to prepare a Python environment for testing AI suggestions
Prepare an isolated Python environment with venv: check the interpreter, separate dependencies and record the test without mistaking isolation for protection.
How to turn checked data into an AI-assisted report
Turn reviewed data into an AI-assisted report: organize evidence, explain limitations and distinguish findings, interpretations and pending decisions.
How to choose charts for an AI-assisted analysis
Choose charts with AI starting from the question: compare alternatives, check scales and units, and verify the message against the original table.
How to analyze customer feedback with AI and clear criteria
Analyze feedback with AI using defined themes, evidence and review. Count messages without confusing a sample with the views of all customers.
How to investigate missing values before AI data analysis
Investigate missing values before AI analysis: distinguish zero from empty fields, check collection and document treatment without inventing information.
How to create a data dictionary with AI assistance
Create a data dictionary with AI: define columns, units, accepted values and missing-data rules, then verify examples and questions with the source owner.
How to evaluate AI answers grounded in documents
Evaluate RAG answers with reference questions: check retrieval, evidence, versions and conditions before expanding access to your AI application.
How to organize documents for an AI knowledge base
Organize an AI knowledge base: select sources, record versions, preserve passage context and test access permissions and document updates.
Keyword or semantic search: how to choose
Compare keyword, semantic and hybrid search using real questions, codes and versions. Learn to test results before choosing a retrieval strategy.
What embeddings are and how they help find information
Understand embeddings without advanced math: numerical representations, similarity search and how to test whether retrieved documents are useful.
RAG in practice: when AI needs to consult your documents
Understand RAG through a practical example: retrieve passages, supply context and answer with evidence while separating retrieval from model training.
How to choose tasks to automate with AI
Choose tasks to automate with AI using clear criteria: understandable inputs, verifiable outputs, exceptions, human review and a limited pilot.
How to document a work process with AI
Turn notes into a work procedure with AI: define the start, inputs, owners and exceptions, validate the draft and record its approved version.
How to check AI-generated calculations and tables
Check AI calculations with a practical method: reproduce arithmetic, review denominators, filters and rounding, and separate numbers from interpretations.
How to prepare a CSV for AI analysis
Prepare a CSV for AI analysis: check columns, dates, delimiters and missing data, create a data dictionary and test a question using a small sample.
How to use AI to plan spreadsheet formulas
Learn to plan spreadsheet formulas with AI: describe columns and rules, test examples and check cell references before filling an entire table.