Building AI Assistants from Your Own Documents

• Large language models and token-based generation
• Capabilities and limitations of LLMs
• Why AI assistants hallucinate
• How AI models learn from data
• Difference between general model knowledge and organization-specific knowledge

• Defining the business problem
• Determining whether AI is appropriate
• Assessing data and document readiness
• Defining realistic outputs
• Evaluating build-versus-buy options against technical requirements
• Defining technical scope and user requirements

• Projects and persistent AI workspaces
• Selecting authoritative documents
• Preparing and cleaning source material
• Creating a document/source index
• Retrieval-augmented generation
• Writing assistant instructions and constraints
• Configuring no-code assistant builders
• Sharing and access controls
• Source citation and grounded responses

Hands-on Labs: Prepare the Knowledge Pack — Prepare a realistic organizational document collection and create a source index. Build the Assistant — Configure a working document-based assistant, load approved knowledge sources, and apply a persistent instruction set.

• Multimodal AI
• PDFs and scanned documents
• Images and screenshots
• Tables and forms
• Extracting structured information from files
• Verifying AI-extracted values against the original source

Hands-on Lab: Extract Information from Files and Images — Use an AI assistant to extract structured information from multiple file types and manually validate the results for accuracy.

• Knowledge-management assistants
• Intelligent document search
• Internal support and service assistants
• Ticket routing
• Legal and compliance document use cases
• Meeting and organizational knowledge workflows

• Data ownership and stewardship
• Data lineage and document lifecycle
• Source freshness
• Retention and quality requirements
• Creating assistant test sets
• Answerable versus unanswerable questions
• Testing groundedness and citations
• Diagnosing inaccurate answers
• Updating sources and instructions

Hands-on Lab: Test and Improve the Assistant — Create a structured test set, measure the assistant before and after improvements, and document the changes that increased answer quality.

• Configuring user onboarding and expectations
• Communicating assistant limitations
• Building appropriate trust
• Collecting user feedback
• Updating knowledge and instructions
• Continuous improvement
• Controlled team rollout

Deliver a working document-based assistant for a defined audience together with its knowledge sources, operating instructions, test evidence, known limitations, maintenance procedure, and user handoff documentation. The capstone is assessed against a skills-based rubric and must be successfully completed to earn the CERT4TECH Certificate in Building Document-Based AI Assistants.