Generative AI for Research and Analytics Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Research Analysts, Business Analysts and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-AI-070

Before you join: Participants should have experience conducting business, policy, market, operational or financial research, or producing analytical reports for decision-makers.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Generative AI can reduce the time required to search, structure, analyse and communicate research, but unreliable outputs can create serious problems. Analysts may receive plausible but unsupported claims, incorrect calculations, weak citations or summaries that omit material risks. Organisations need staff who can use generative AI to accelerate research and analytics while retaining control over evidence quality, confidentiality, analytical logic and decision accountability.

This course teaches participants to frame research questions, design evidence plans, write controlled prompts, retrieve and assess sources, analyse data and communicate findings. Participants learn how generative AI supports tasks such as literature scanning, source synthesis, SQL and Python drafting, data cleaning, descriptive analysis, scenario development and executive reporting. The course also addresses hallucination testing, source triangulation, reproducibility, privacy, copyright, vendor review and approval controls.

Teaching combines instructor briefings with case work, group analysis, prompt reviews, research simulations and practical planning. Cases reflect decisions common in banking, public services, NGOs, corporate strategy, operations and market research. Participants build reusable research protocols, prompt patterns, evidence tables, evaluation rubrics and governance controls. They leave with a structured 90-day plan for applying generative AI to a defined research or analytics workflow.

The course suits professionals who already conduct research, produce analysis or manage analytical teams and now need disciplined methods for using generative AI in work that must withstand review.

What you will be able to do

  • Frame research questions using SMART objectives, hypothesis trees and measurable decision criteria
  • Design evidence plans using source hierarchies, inclusion criteria and research protocols
  • Write controlled prompts using role, context, constraints, examples and output schemas
  • Evaluate generated research using triangulation, citation tracing and hallucination testing
  • Analyse structured data using AI-assisted SQL, Python specifications and descriptive statistics
  • Validate analytical outputs using evaluation rubrics, reproducibility logs and confidence labels
  • Apply NIST AI RMF controls to privacy, copyright, vendor and accountability risks
  • Produce an AI-supported research workflow and 90-day implementation plan

Benefits of attending

For you

  • Produce traceable research outputs with clearer evidence and citation records
  • Reduce time spent drafting research briefs, data queries and first-pass summaries
  • Assess AI-generated claims before presenting them to senior decision-makers
  • Review generated SQL and Python with greater confidence and technical precision
  • Build a credible AI research use case for a current role or team

For your organisation

  • Shorten research cycle times while retaining documented evidence controls
  • Reduce unsupported claims and citation failures in analytical reporting
  • Create repeatable prompt, review and approval standards across research teams
  • Protect sensitive data through clearer input rules and vendor assessment
  • Prioritise AI research use cases using value, feasibility, risk and ownership criteria

Course modules

10 modules over 5 days

Participants can identify suitable AI-supported research tasks and establish human review points.

  • Generative model capabilities, limitations and failure modes in research workflows
  • Transformer, token, context window and temperature concepts for reliable prompting
  • Distinguishing generative AI, predictive analytics, machine learning and business intelligence
  • Research lifecycle mapping from question formulation through decision-ready communication
  • Human judgement checkpoints for claims, calculations, sources and recommendations
  • Task selection matrix using value, risk, data sensitivity and repeatability

Participants can convert a business request into a structured and testable research protocol.

  • Problem statements translated into decision questions, assumptions and measurable criteria
  • SMART research objectives and hypothesis trees for ambiguous business requests
  • MECE issue trees separating drivers, constraints, stakeholders and evidence needs
  • Source hierarchy ranking primary data, official statistics, literature and commentary
  • Inclusion and exclusion criteria documented before collecting external evidence
  • Research protocol defining scope, search terms, provenance and stopping rules

Participants can create prompts that generate controlled, auditable and usable research outputs.

  • Prompt architecture using role, task, context, constraints and output schema
  • Few-shot prompting with positive examples, counterexamples and acceptance criteria
  • Structured outputs specified through JSON schemas, tables and controlled vocabularies
  • Chain-of-thought replaced by concise reasoning requests and verification steps
  • Prompt versioning records purpose, inputs, model settings and observed failures
  • Agentic workflows bounded by tool permissions, handoffs and human approval

Participants can retrieve, assess and document sources for AI-supported research findings.

  • Search strategies combining Boolean operators, site filters and authoritative domains
  • Retrieval-augmented generation grounded in curated documents and citation requirements
  • Document chunking, metadata and embeddings for targeted retrieval
  • Source triangulation tests agreement across independent primary and secondary evidence
  • Citation tracing distinguishes direct support, inference and unsupported model claims
  • Evidence tables capture source, date, claim, confidence and limitations

Participants can use generative AI to support structured analysis while retaining analytical control.

  • Analytical question translated into variables, measures, segments and comparison groups
  • Data quality profiling identifies missingness, duplicates, outliers and inconsistent codes
  • Descriptive statistics selected for distributions, rates, central tendency and dispersion
  • Cross-tabulations and cohort analysis reveal patterns within defined populations
  • Generative AI-assisted SQL reviewed against schemas, joins and business definitions
  • Analysis plans separate exploratory findings from confirmatory tests and decisions

Participants can review AI-generated code and present analytical findings with appropriate visual evidence.

  • Python notebook prompts generate transparent data-cleaning and analysis code
  • SQL query specifications constrain tables, filters, aggregations and join logic
  • Code review checks generated scripts for errors, security and reproducibility
  • Chart selection matches comparison, distribution, relationship and composition questions
  • Data storytelling links visual evidence, interpretation, uncertainty and recommended action
  • Executive dashboards distinguish operational monitoring from one-off analytical investigation

Participants can test AI-generated research and analysis before it informs a decision.

  • Hallucination testing uses known-answer questions, adversarial prompts and source checks
  • Evaluation rubrics score factuality, relevance, completeness, citation quality and usability
  • Statistical validation checks sample sizes, baselines, confounders and significance claims
  • Reproducibility logs record datasets, prompts, model versions and processing steps
  • Confidence labels distinguish verified evidence, plausible inference and unresolved uncertainty
  • Red-team reviews probe bias, unsafe advice, omissions and misleading summaries

Participants can apply practical governance controls to AI-supported research and analytics activities.

  • NIST AI RMF functions mapped to research and analytics use cases
  • Data classification determines permitted inputs, retention periods and access controls
  • Personally identifiable information removed through minimisation, masking and aggregation
  • Vendor assessment reviews data use, model training, security and audit provisions
  • Copyright and licensing checks govern uploaded material and generated outputs
  • Approval gates assign accountability for high-impact research findings and decisions

Participants can turn validated AI-supported analysis into clear and defensible decision materials.

  • Evidence synthesis separates facts, assumptions, interpretations, options and recommendations
  • Executive briefs structure decision context, key findings, risks and actions
  • Claim-evidence-reasoning chains make analytical conclusions inspectable and defensible
  • Scenario analysis presents ranges, drivers, sensitivities and decision triggers
  • Stakeholder-specific communication adapts detail, terminology, visuals and disclosure
  • Peer review protocol checks accuracy, traceability, clarity and decision relevance

Participants can plan a controlled pilot for an AI-supported research or analytics workflow.

  • Use-case prioritisation scores value, feasibility, risk, data readiness and ownership
  • Research workflow redesign assigns human review points and model responsibilities
  • Standard operating procedures define approved prompts, sources, checks and escalation
  • Performance metrics track cycle time, evidence quality, error rates and adoption
  • Change plan addresses skills, stakeholder concerns, governance and communications
  • Ninety-day application plan specifies pilot scope, milestones, owners and measures

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. AI research protocol template
  2. Controlled prompt pattern library
  3. Source evaluation and evidence table
  4. AI output evaluation rubric
  5. Research governance checklist
  6. Ninety-day implementation plan

Who should attend

  • Research Analysts — need faster evidence gathering without weakening source discipline
  • Business Analysts — need controlled methods for AI-supported requirements, analysis and reporting
  • Data Analysts — need to assess and use generated SQL, Python and analytical outputs
  • Policy and Programme Officers — need defensible research for public, donor and stakeholder decisions
  • Strategy and Market Intelligence Managers — need reliable synthesis of market, competitor and sector evidence
  • Analytics and Research Leaders — need governance controls for team adoption and review

Skills and tools covered

AI research designPrompt engineeringEvidence validationAI-assisted analyticsResearch governanceDecision communication

Tools and standards

NIST AI RMF 1.0, CRISP-DM, JSON Schema, Retrieval-augmented generation (RAG)

How the course runs

Training methodology

The course uses short instructor briefings to introduce methods, followed by case work drawn from research, analytics and decision-reporting assignments. Participants assess sources, refine prompts, inspect generated analyses, test outputs against evidence and compare alternative research approaches. Group exercises focus on evidence tables, evaluation rubrics, governance decisions and executive communication. Simulations expose common failures, including fabricated citations, misleading summaries and incorrect query logic. Each participant develops an application plan for a research or analytics workflow from their own professional context.

Requirements and prerequisites

Participants should have experience conducting business, policy, market, operational or financial research, or producing analytical reports for decision-makers. They should be comfortable working with spreadsheets, tables, charts and online sources, and able to explain how they currently check evidence and communicate findings. Familiarity with at least one generative AI assistant is useful but not essential. Programming, data science, statistics beyond basic descriptive measures, model development and prior experience with SQL or Python are not required. The course explains AI-assisted SQL and Python review without assuming coding proficiency.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should have experience with business, policy, market, operational or financial research, or with analytical reporting. Basic spreadsheet and chart literacy is expected; coding and advanced statistics are not required.

Bring a laptop if attending in person, together with non-sensitive examples of research or analysis tasks you want to improve. Do not bring confidential datasets, client documents or personally identifiable information unless your organisation has approved their use.

It is designed for analysts, researchers, strategy staff, policy officers, market intelligence professionals and managers of research or analytics teams. It is most useful for people whose outputs must be evidence-based and suitable for review.

The course focuses on the full research and analytical workflow: framing questions, assessing sources, validating claims, analysing data, governing use and communicating decisions. It does not train participants to build foundation models or become specialist machine learning engineers.

Participants can apply the research protocol, prompt patterns, evidence table and output evaluation rubric to an existing assignment. The final module helps translate one selected use case into a controlled pilot with owners, review points and measures.

The five-day format provides about 32.5 contact hours, delivered from 08:30 to 16:30 each day. Time is divided between instructor input, case analysis, prompt and evidence reviews, simulations, group work and individual application planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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