Snowflake Cortex AI Application Development Training Course

Course overview

Snowflake teams are under pressure to turn governed enterprise data into useful AI experiences without exporting sensitive records to separate model platforms or relying on uncontrolled prompt prototypes. This course addresses the practical work of building production-oriented applications with Snowflake Cortex: natural-language data assistants, document-processing workflows, retrieval-augmented generation (RAG) interfaces, and task-specific AI functions. Participants learn how to make these applications accurate, secure, measurable, and aligned with Snowflake role-based access controls, rather than treating generative AI as an isolated experiment.

The course covers Cortex AI SQL functions, Cortex Search, Cortex Analyst semantic models, and Streamlit in Snowflake application patterns. Participants write and test prompts, use structured outputs, classify and extract information from documents, create searchable knowledge indexes, and configure semantic models that translate business questions into trustworthy SQL. They also examine model selection, token and credit consumption, evaluation datasets, guardrails, RBAC, and application deployment decisions. The emphasis is on selecting the right Cortex capability for a defined business use case and implementing it with reproducible SQL, Python, and application assets.

Instruction combines focused technical demonstrations with guided labs in a Snowflake environment. Each day uses a realistic enterprise scenario, such as policy-document analysis, service-request triage, or self-service analytics, to connect features to operational requirements. By the final day, each participant has built and presented a working AI application blueprint: a Streamlit interface backed by Cortex Search and/or Cortex Analyst, with prompt designs, semantic-model assets, access controls, evaluation criteria, and a rollout plan. Participants receive a certificate on completion.

The course is best suited to data professionals, analytics engineers, developers, and technical product owners who already work with Snowflake and need to deliver governed AI capabilities within their organisation's data platform.

Course objectives

By the end of this course, participants will be able to:

• Design a Snowflake Cortex application architecture that maps business requirements to AI SQL functions, Cortex Search, Cortex Analyst, and Streamlit components
• Write and test AI_COMPLETE prompts that produce constrained, structured outputs for enterprise data workflows
• Build SQL pipelines using Cortex AI functions for classification, extraction, summarisation, translation, and document parsing
• Create a Cortex Search service with an appropriate source table, searchable attributes, filters, and retrieval test cases
• Author a Cortex Analyst semantic model in YAML with verified metrics, dimensions, joins, synonyms, and sample questions
• Develop a Streamlit in Snowflake interface that combines user inputs, Cortex responses, citations, and governed data access
• Evaluate AI application quality using a labelled test set, prompt variants, retrieval checks, SQL validation, and cost measures
• Produce an implementation blueprint containing RBAC controls, monitoring measures, deployment steps, and acceptance criteria

Benefits of attending

Target competencies

Who should attend

• Snowflake Developers — who need to build governed generative AI applications directly on the Snowflake platform
• Data Engineers — who prepare source data, pipelines, and access patterns for Cortex-powered workloads
• Analytics Engineers — who define trusted metrics and semantic models for natural-language analytics
• Data Scientists — who need to combine LLM capabilities with enterprise data without moving governed datasets
• BI and Analytics Leads — who must introduce self-service data experiences while protecting metric definitions
• Technical Product Owners — who need to scope, evaluate, and govern internal AI use cases with delivery teams

Requirements and prerequisites

Participants should be comfortable navigating Snowsight, running SQL queries, and working with tables, views, roles, and warehouses in Snowflake. Practical familiarity with joins, basic data modelling, and Python fundamentals is assumed; participants will read and adapt short Python and Streamlit examples, not build a software system from scratch. Prior exposure to generative AI concepts such as prompts, tokens, embeddings, and RAG is helpful but not essential. No machine-learning model training, deep-learning framework experience, or external cloud AI account is required. Participants should have access to a Snowflake training account with permissions to create the course objects.

Training methodology

The five-day programme alternates instructor-led architecture sessions with guided build labs in Snowflake. Participants inspect source tables, write Cortex AI SQL, configure a Cortex Search service, and edit Cortex Analyst YAML rather than relying on slide-only examples. Short case studies require teams to choose an appropriate AI pattern and defend trade-offs involving accuracy, access control, latency, and credits. Daily exercises add tested assets to a single application scenario. The final workshop combines these assets into an implementation plan and peer-reviewed demonstration.

Course outline

Day 1: Cortex architecture and AI SQL foundations

• Snowflake Cortex service architecture and regional availability considerations
• Selecting AI SQL functions for generation, classification, extraction, translation, and summarisation
• AI_COMPLETE prompt construction with system instructions and structured response formats
• Using AI_CLASSIFY and AI_EXTRACT for repeatable operational data tasks
• Document ingestion patterns and AI_PARSE_DOCUMENT output handling
• Model selection, token accounting, and credit-consumption trade-offs
• Role-based access control and data-governance implications for Cortex workloads

Workshop: Participants build a SQL-based service-request triage workflow that classifies, summarises, and extracts priority fields from unstructured requests.

Day 2: Retrieval-augmented applications with Cortex Search

• RAG architecture choices within the Snowflake data platform
• Preparing chunked document tables and searchable metadata attributes
• Creating and refreshing a Cortex Search service
• Configuring search columns, attribute filters, and access-aware source data
• Querying Cortex Search from SQL and Python application code
• Designing citation-aware prompts from retrieved document passages
• Testing retrieval relevance, filter behaviour, and unsupported-answer handling

Workshop: Participants create a searchable policy knowledge base and produce a retrieval test sheet covering relevant, irrelevant, and permission-restricted queries.

Day 3: Natural-language analytics with Cortex Analyst

• Cortex Analyst architecture and the semantic-layer role in text-to-SQL
• Designing analytical tables, dimensions, facts, and governed metric definitions
• Authoring semantic model YAML files and logical table specifications
• Defining joins, relationships, synonyms, and verified query examples
• Using business terminology to improve natural-language question interpretation
• Testing generated SQL for correctness, ambiguity, and data-access compliance
• Versioning and maintaining semantic models as source schemas change

Workshop: Participants author a semantic model for a sales or service dataset and validate Analyst responses against a set of business questions.

Day 4: Building governed Streamlit AI applications

• Streamlit in Snowflake application architecture and execution context
• Creating user interfaces for chat, document questions, and analytical requests
• Calling Cortex Search and Cortex Analyst from a Streamlit application
• Managing session state, user input validation, and response rendering
• Displaying source citations, generated SQL, and confidence-oriented user guidance
• Applying Snowflake roles, secure views, and least-privilege application access
• Packaging application code, database objects, and environment dependencies for release

Workshop: Participants build a Streamlit prototype that routes user questions to a document-search or semantic-analytics experience and displays the resulting evidence.

Day 5: Evaluation, operations, and application rollout

• Defining acceptance criteria for accuracy, relevance, safety, latency, and cost
• Creating labelled evaluation datasets and representative user-query suites
• Comparing prompt variants, model choices, and retrieval configurations
• Validating generated SQL, hallucination controls, and citation completeness
• Monitoring query history, token usage, warehouse consumption, and application failures
• Establishing release controls, feedback loops, and incident-response ownership
• Preparing a phased production rollout and stakeholder adoption plan

Workshop: Participants present an end-to-end Cortex application blueprint containing a prototype walkthrough, evaluation plan, RBAC design, cost assumptions, and production rollout backlog.

Tools & standards covered

Snowflake Cortex AI, Cortex Search, Cortex Analyst, Streamlit in Snowflake

A typical training day

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

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions