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Claude for Consultants and Client-Facing Teams
Duration
1 Day
Course
CL-050
Available Formats
Classroom Training, Online Training
Course Description
Overview
This Claude for Consultants and Client-Facing Teams course is designed to move business professionals from ad hoc chat use to configured, defensible Claude workflows they can run in front of a client. Participants select the product surface and model that fit a stated business task, decompose the task into prompts that hold under pressure, validate what Claude returns before it reaches a client or a compliance audience, configure a Project with instructions and knowledge sources that a colleague can pick up cold, rule on the data-sensitivity and policy questions that real requests raise, and draw the line where a Project stops being sufficient and the work belongs to a developer or an architect. Every decision is proved by building the thing and testing it: participants design a workflow, build it, and measure the output against a source of truth.Objectives
Upon completion of the Claude for Consultants and Client-Facing Teams course, students will be able to:
- Select the Claude product surface and feature — chat, Projects, research mode, Artifacts — that fits a stated business task
- Select between Haiku, Sonnet, and Opus against a task's cost, speed, and quality requirements, and manage context limits by restarting, summarizing, or persisting
- Decompose a complex business request into a structured prompt sequence and iterate it against a stated quality bar
- Evaluate Claude output for accuracy, completeness, and bias, and identify hallucinated detail before it reaches a client
- Determine when an output requires human review, additional verification, or escalation to a developer or architect
- Configure a Claude Project with system-level instructions, knowledge sources, and connectors that a colleague can operate without coaching
- Apply data-sensitivity, privacy, and organizational-policy judgment to a request involving regulated information
- Diagnose an underperforming prompt or workflow and remediate it, then optimize the workflow for time, cost, and consistency
- Scope a client engagement, communicate Claude's value and limitations to stakeholders, and write the handoff brief for work that exceeds the Associate boundary
Audience
- Consultants, client-facing professionals, and knowledge workers who use Claude as a productivity tool and build Claude Projects in their day-to-day roles — operations, marketing, project management, communications, education, and business analysis — including internal staff who maintain and optimize AI-enabled workflows and external consultants who support implementation, use-case identification, and process redesign.
- Candidates for the Claude Certified Associate – Foundations (CCAO-F) credential.
Prerequisites
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No mandatory prerequisites and no prior LearnQuest course required. Participants should bring regular, hands-on experience using Claude in a professional setting, a foundational grasp of structured problem-solving and workflow design, and a practical understanding of AI limitations including hallucination, context constraints, and data sensitivity — the profile Anthropic recommends for the CCAO-F candidate. No software-development, API, or machine-learning experience is required or assumed.
Topics
- Claude.ai, Projects, Artifacts, research mode, Skills, Connectors, and Claude Cowork
- Where Claude Code and the Claude API begin, and why that boundary marks a handoff rather than a next step (building on those surfaces is the subject of the Developer track, CL-100A and CL-250)
- Choosing the surface before choosing the prompt
- Hands-on lab: map three live work tasks to the product feature that actually fits each one, and justify the one you rejected
- Haiku, Sonnet, and Opus: capability, latency, and cost, in business rather than benchmark terms
- Matching the model to the task: high-volume drafting where speed and cost dominate, versus a single high-stakes analysis where they do not
- Context limits and memory: when to restart a conversation, when to summarize, and when to persist knowledge in a Project
- Hands-on lab: run one task on two models, measure the difference in quality, latency, and cost, and defend the choice you would bill for
- Structure that holds: role, context, task, constraints, and the shape of the expected output
- Decomposing a complex request into ordered steps that can be checked one at a time
- Adapting strategy by task type: analysis, research, drafting, and brainstorming
- Iterating toward a quality bar, and recognizing the point at which further iteration stops paying
- Hands-on lab: take a vague client request and decompose it into a prompt sequence that produces usable work
- Accuracy and completeness against a source of truth, not against the confidence of the prose
- Hallucination patterns: fabricated citations, invented figures, and plausible-but-wrong specifics
- Inconsistency and bias in generated content, and how to surface both
- Fact-checking technique, and the level of verification a compliance or client audience requires
- Editing, adapting, and comparing outputs for a specific audience
- Selecting the output format: Artifact, inline response, or structured data
- Hands-on lab: audit a seeded Claude deliverable containing planted errors, and produce a validation record a client would accept
- Project instructions as the system-level layer: what belongs there and what belongs in the prompt
- Knowledge sources: what to upload, what to leave out, and how to keep it current
- Connectors, including Google Drive and Gmail, and what changes once Claude can reach live data
- Maintaining and updating a configuration as the work, the client, and the policy change
- Hands-on lab: build a working Project for a recurring engagement task, then hand it to another participant and watch them run it cold
- Appropriate and inappropriate use cases, and how to tell the difference before the request is answered
- Regulated and sensitive data: removing or anonymizing identifiers before upload so the analysis can still proceed
- Why an instruction to the model is not a data control
- Organizational AI policy and governance standards as a working constraint, and the ethical implications of AI use in client work
- Hands-on lab: rule on a set of real-world requests — proceed, modify, redact, or decline — and write the justification you would give the client
- Reading an existing workflow and deciding where Claude augments it and where it should redesign it
- Requirements analysis, use-case identification, and process optimization with Claude in the loop
- Communicating value and limitation to a stakeholder who has heard only the marketing
- The escalation boundary: when a Project is sufficient, and when the work belongs to a developer or an architect
- Hands-on lab: scope an engagement and write a handoff brief a developer or architect could act on without a meeting
- Diagnosing a poor output: is the fault in the prompt, the context, the knowledge source, the model, or the task itself
- Adjusting on feedback and results rather than restarting from scratch
- Optimizing a workflow that already works, for time, cost, and consistency
- Hands-on lab: repair a failing workflow and measure the improvement against its baseline
- Scope a realistic client engagement task and decide what Claude should and should not do inside it
- Build the workflow: a configured Project with instructions, knowledge sources, and a defended model selection
- Validate its output against a source of truth and produce the validation record that goes with the deliverable
- Rule on the data-sensitivity and policy questions the engagement raises, and document the calls
- Draw the escalation boundary and write the handoff brief for the work that exceeds it
- Present the workflow to a stakeholder audience and defend the decisions behind it
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- Standalone learning or supplemental reinforcement.
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