Best Practices - praxis-ai

This guide shows how to get the most out of Pria with precise prompts, complete context via IP Vault, verifiable outputs with Tool Details, and repeatable workflows using Assistants. It aligns with Praxis AI’s operating model and links to relevant documentation where applicable. To unlock the full potential of Praxis AI’s Pria and ensure optimal, precise, and efficient interactions, integrate these ten best practices into your workflow. By doing so, you can significantly enhance the quality of responses, streamline research, and effectively manage your AI-driven tasks.

At a glance

1- Be specific

Craft precise, bounded prompts with clear deliverables, tone, and constraints.

2- Use IP Vault for websites

Scrape full sites to give Pria clean, complete context instead of noisy partial HTML.

3- Verify with Tool Details

See which tool ran, sources used, success status, and returned data.

4- Manage conversations

Create and choose the right conversation to preserve context and retrieve history.

5- Set perspective

Tell Pria which role to adopt for audience-aligned output.

6- Create Assistants

Automate multi-step workflows with clear, ordered instructions and error handling.

7- Apply CRISPE

Design robust Assistants with a structured capability and context template.

8- Iterate & validate

Use Tool Details to identify gaps and refine prompts or sources accordingly.

9- Stay organized

Name conversations and Assistants consistently for easy retrieval.

10- Respect governance

Follow data, privacy, and safety rules using IP Vault and RAG over ad-hoc copy-paste.

1 Be Specific: The power of detailed requests and context

The foundation of effective interaction with Pria lies in the specificity and depth of your prompts. A well-crafted request, rich in detail and relevant context, directly correlates with the quality and accuracy of the AI’s response. Specificity directly improves precision and reduces rework.

Think of your prompt as a detailed instruction manual for Pria. The more precise you are, the better the results.

Why Specificity Matters

Avoid vague or ambiguous language. Clearly articulate what you need, including any specific parameters, desired formats, or limitations. A detailed request helps Pria understand exactly what you’re looking for and deliver accurate, relevant responses.

Building Specific Prompts

State the objective and audience

What should be produced and for whom (e.g., teachers vs. executives). Define the purpose and target audience clearly.

Constrain format and scope

Word count, headings, components, style, and what to exclude. Set boundaries for length, structure, and content.

Provide source context

Attach RAG/IP Vault content or quote key excerpts. Give Pria access to the information sources it should use.

Define success criteria

Checks, tests, or acceptance conditions. Explain how to evaluate if the output meets your needs.

Example: Generic vs. Specific

Generic: “Write about marketing”Specific: “Write a 500-word blog post on the latest trends in digital marketing for small businesses, focusing on SEO strategies and social media engagement, in a conversational yet authoritative tone.”

Complete Example Prompt

Objective: Draft a 500–600 word announcement for small businesses about Q1 SEO trends.
Audience & tone: Conversational yet authoritative; non-technical.
Format: 4 sections (Overview, Trend 1, Trend 2, Actions); include 3 actionable bullets.
Constraints: Cite any stats inline; avoid jargon like "synergy"; no images.
Sources: Use my IP Vault document for "AcmeSEO Blog Q1 2025" and my RAG file "Search Trends Notes".
Success: Clear 90-day action plan and linked glossary terms.

2 Website research via IP Vault: Comprehensive scraping

When gathering information from websites, leverage the IP Vault for comprehensive data acquisition rather than simply asking Pria to “look at this website.” This method offers significant advantages in terms of content depth and cleanliness.

Why IP Vault?

Full-context capture

Pulls complete site content for deep analysis, not just partial HTML. Utilizing the IP Vault to scrape a site allows you to capture the entire content, providing Pria with the complete context needed for thorough analysis and accurate response generation.

Clean data

Filters out extraneous JS/CSS and delivers structured text. Direct URL reading by Pria typically yields only about 20% of the content, often intermingled with extraneous HTML and JavaScript. Scraping via the IP Vault ensures a much cleaner, more comprehensive dataset.

Repeatable

Documents are reusable across conversations and Assistants. Once scraped, the content can be referenced multiple times without re-scraping.

The Problem with Direct URL Reading

Direct URL reading by Pria often results in:

Only ~20% of actual content captured
Content mixed with HTML, JavaScript, and CSS code
Incomplete context for analysis
Noisy, unstructured data

Using IP Vault Effectively

Create a site snapshot file

Add the site to IP Vault and complete the scrape. This captures the full website content in a clean, structured format.

Name and tag consistently

Use descriptive names like VendorX Docs (2025-10-02) | pricing, api, auth to make documents easy to find and reference.

Reference documents explicitly

Tell Pria which file to use by name to ensure it uses the right source.

Limit scope as needed

Ask for specific sections to avoid noise and focus on relevant content.

Refresh on updates

Re-scrape periodically when content changes to keep your data current.

Example Usage

Use IP Vault: "Compare pricing tiers from 'VendorX Docs (2025-10-02)' and map features to our needs.
Avoid direct URL reads; use my document only."

This eliminates the limitations often encountered when Pria directly reads URLs and improves Pria’s ability to process and synthesize information effectively.

3 Tool Details: Unveil process and verification

The “Tool Details” feature is an invaluable tool for understanding how Pria constructs its responses and verifying the integrity of the information provided. Tool Details shows which tool executed, whether it succeeded, which sources were used (RAG, IP Vault, browser), and what data was returned.

This transparency is crucial for assessing the reliability and originality of the AI’s output.

What Tool Details Reveals

Understanding Response Compilation

By examining Tool Details, you can discern which sources and methods Pria employed to compile its response. This insight helps you differentiate between information directly retrieved (e.g., from a search in RAG via call_rag or the internet via get_browser) and content that might be inferred or generated by the LLM.Key information includes:

Tool identity and version
Execution duration
Input parameters used
Server label (mcp)
Response Text

Assessing Success and Returns

The details indicate which tool was used, whether it was successful in its operation, and what specific data or output it returned. This allows you to:

Verify successful execution
Identify error messages and failure points
Review source breakdown (RAG sections, IP Vault documents, web fetches)
Examine returned artifacts (tables, JSON, files)

Source Verification

Tool Details shows exactly which sources were consulted:

RAG documents and specific sections
IP Vault files referenced
Web searches performed
API calls made

This transparency helps you validate that Pria used the correct and most relevant sources.

How to act on gaps

Use the information in Tool Details to improve your interactions:

If sources are missing: Attach or reference the right RAG/IP Vault items
If a step failed: Retry with narrower scope or corrected parameters
If inference is high: Ask Pria to separate “retrieved facts” vs “model inferences”
If output quality is low: Identify which steps succeeded and which need refinement

Why This Matters

Tool Details helps you identify potential gaps in information retrieval or areas where Pria might have had to “fill in the blanks,” guiding you on when further investigation or clarification might be needed.

Always review Tool Details for critical or high-stakes outputs to ensure the response is built on reliable, verifiable sources rather than model inference.

4 Manage conversations: Organize context and history

Effective conversation management is essential for maintaining context and preventing information loss within your interactions with Pria. Each new thought or topic should ideally initiate a new conversation.

Why Conversation Management Matters

Pria’s responses are heavily influenced by the current conversation’s history. Selecting the appropriate conversation ensures that Pria has access to all relevant prior interactions, leading to more coherent and accurate responses.

Contextual Integrity

Conversations organize your dialogues and maintain the context that Pria uses to generate responses. Proper conversation management ensures:

Context Preservation: Pria remembers relevant details from earlier in the conversation
Accuracy: Responses build on established context rather than starting fresh
Coherence: Multi-turn interactions maintain logical flow and reference earlier points

Best Practices for Conversation Management

Start a new conversation per topic

Prevents cross-topic contamination. Each distinct topic or project should have its own conversation thread.

Pick the correct conversation before asking

Ensures relevant history is available. Before asking a question, verify you’re in the right conversation context.

Name conversations descriptively

Use clear, specific names like “Mintlify Writer — Pria Best Practices” or “Q4 Marketing Campaign Planning” to make conversations easy to identify and retrieve.

Leverage 'Clear selected' to broaden history

Allows Pria to draw across prior threads when appropriate. This broadens Pria’s contextual understanding, allowing it to draw upon a wider range of past interactions.

Organization and Retrieval

If something “disappears,” check a different conversation thread. Context is scoped per thread by design. If you suspect information has been lost, it might simply be part of a different conversation thread.

Assistant-Driven Workflows

Conversations are often created in conjunction with Assistants, which are designed to execute specific instructional workflows. Aligning your questions with the correct conversation ensures Pria applies the appropriate Assistant’s logic and context.

Create a naming convention for your conversations that includes the project, date, or Assistant type to make them easier to find later.

5 Use perspective: Define Pria’s role explicitly

Directing Pria to adopt a specific persona or role can significantly enhance the relevance and tone of its responses. By framing your request with a defined perspective, you guide Pria to generate output that aligns with that particular expertise.

Why Perspective Matters

Explicitly stating Pria’s desired role ensures the output resonates with the intended audience and purpose. The perspective you set determines:

Vocabulary and terminology used
Depth and technical level of explanations
Tone and communication style
Focus and priorities in the response

Examples of Effective Perspective Setting

Social Media Expert

"As a social media strategist, create a 7-day content plan prioritizing short-form video
for small retailers. Include hooks, CTAs, and platform-specific notes."

AI Engineer

"As an AI engineer, outline a Node service to summarize PDFs using our IP Vault documents.
Include architecture, error handling, and timeouts."

Business Analyst

"As a business analyst, review our Q3 sales data and identify the top 3 trends
impacting revenue. Present findings in executive summary format."

Technical Writer

"As a technical writer, document this API endpoint with clear examples,
parameter descriptions, and common error scenarios."

Tailored Communication

When you instruct Pria to act as an “AI engineer,” it will leverage its understanding of AI principles and development to “build an app for me that does A, B, and C,” providing solutions and insights relevant to that technical domain.

Setting Effective Perspectives

State domain, deliverable, constraints, and evaluation criteria in the first message to lock in the perspective. This ensures consistent output aligned with your needs.

Template for perspective setting:

As a [ROLE], [ACTION] for [AUDIENCE/PURPOSE].
Include [SPECIFIC ELEMENTS].
Use [TONE/STYLE].
Focus on [PRIORITIES].

The perspective you set influences every aspect of Pria’s response, from word choice to structural organization to the depth of technical detail provided.

6 Create Assistants: Automate and optimize workflows

The ability to create custom Assistants within Praxis AI is a powerful tool for automating repetitive tasks and streamlining complex operational workflows. Developing this skill is a crucial aspect of mastering prompt engineering.

Why Create Assistants?

Automation

Assistants are designed to follow a defined set of step-by-step instructions, effectively automating routine or multi-stage processes. This frees up your time for more strategic tasks.

Consistency

Structured operational workflows ensure consistency and accuracy in task execution across multiple runs.

Repeatability

Turn reliable processes into repeatable, multi-step runs with consistent quality.

Optimization

Refine and optimize workflows over time based on performance and outcomes.

Assistant Core Pattern

Assistants turn reliable processes into repeatable, multi-step runs with consistent quality. An Assistant’s instructions define a clear, sequential workflow for the AI to follow.

Basic Assistant Template

You are the "XXX" assistant and your role is to create YYY by following the step-by-step instructions below carefully:

1- Collect input from user through conversation
2- Analyze the data
3- Generate your response
4- End of job

Error Handling:
A- Not enough or invalid input data: Continue or restart at step 1
B- Off topic: Redirect off topic back to the focus of this assistant

Key Elements of Effective Assistants

Clear role definition

Start by defining what the Assistant is and what it produces.

Sequential steps

Break down the workflow into numbered, atomic steps that are easy to follow and verify.

Error handling

Define what happens when things go wrong or inputs are invalid.

Termination condition

Specify when the Assistant’s job is complete.

Keep steps atomic, verifiable, and source-aware (RAG/IP Vault names). Add logging expectations and acceptance checks for robust operation.

Advanced Considerations

When designing Assistants:

Name sources explicitly: Reference specific RAG or IP Vault documents by name
Include validation steps: Check inputs before processing
Define success criteria: What does “done correctly” look like?
Plan for edge cases: How should the Assistant handle unexpected inputs?

Start with simple Assistants and gradually add complexity as you understand the patterns that work well for your use cases.

7 Error handling patterns for Assistants

Building robust Assistants requires thoughtful error handling and validation strategies. This ensures your Assistants can gracefully handle unexpected situations and provide useful feedback.

Core Error Handling Patterns

Validate inputs

Check required fields and data types before processing. Ask targeted follow-ups when information is missing or unclear.

Constrain sources

Prefer specific RAG/IP Vault documents over free web searches. This ensures consistent, reliable information sources.

Plan-then-execute

Have the Assistant outline its plan before executing. This allows for validation and course correction early in the process.

Degrade gracefully

Return partial results plus a “next steps” list when complete execution isn’t possible. Never fail silently.

Error Categories to Handle

Input Errors

Scenario: User provides incomplete, invalid, or ambiguous input.Response Strategy:

List specific fields that are missing
Provide examples of valid inputs
Ask targeted questions to gather needed information
Offer to restart from step 1 with clear inputs

Source Errors

Scenario: Referenced documents or data sources are not available.Response Strategy:

Identify which sources are missing
Suggest alternative sources if available
Ask user to provide or specify the correct sources
Explain what can and cannot be completed without the sources

Processing Errors

Scenario: An intermediate step fails or produces unexpected results.Response Strategy:

Identify which step failed and why
Present what was successfully completed
Offer options to retry with different parameters
Suggest breaking the task into smaller steps

Scope Errors

Scenario: User request is off-topic or outside the Assistant’s capabilities.Response Strategy:

Acknowledge the request
Explain the Assistant’s specific focus and capabilities
Redirect to the intended workflow
Suggest alternative Assistants or approaches if applicable

Example Error Handling Implementation

Error Handling Protocol:

A- Invalid or Incomplete Input:
   - Identify missing required fields
   - Provide examples of valid input format
   - Ask: "Please provide [specific field] in [format]. Example: [example]"
   - Offer to restart from step 1

B- Source Not Available:
   - Report: "Cannot access [source name]"
   - Ask: "Please verify the document name or provide an alternative source"
   - List available alternatives if any exist

C- Processing Failure:
   - Report what was completed successfully
   - Explain where the failure occurred
   - Suggest: "We can try [alternative approach] or adjust [parameters]"

D- Off-Topic Request:
   - Acknowledge: "I understand you're asking about [topic]"
   - Clarify: "This Assistant focuses specifically on [scope]"
   - Redirect: "For [off-topic], please use [appropriate resource/Assistant]"

Well-designed error handling transforms frustrating failures into learning opportunities and improves the overall user experience.

8 Advanced design with CRISPE

For more sophisticated and robust Assistants, explore the CRISPE methodology. This advanced template provides a structured approach to defining your Assistant’s capabilities, context, and desired outputs.

Why CRISPE?

CRISPE is a structured methodology to define Capabilities, Role, Inputs, Steps, Policies, and Evaluation for robust, auditable Assistants. It ensures comprehensive Assistant design that covers all aspects of operation.

The CRISPE Framework

Capabilities & constraints

What the Assistant can and cannot do. Define the boundaries of functionality clearly to set appropriate expectations.

Role & audience

Who it is and who it serves. Establish the persona and target user base for appropriate tone and depth.

Inputs & sources

Required fields and named RAG/IP Vault assets. Specify exactly what information the Assistant needs to operate effectively.

Steps & checkpoints

Atomic tasks with intermediate validations. Break down the workflow into verifiable stages with clear success criteria.

Policies & safety

Privacy, redaction, and compliance boundaries. Establish guardrails for data handling and output generation.

Evaluation & QA

Acceptance tests and quality thresholds. Define how to measure success and what constitutes acceptable output.

CRISPE Template Structure

Assistant: [NAME]

CAPABILITIES:
- Can: [list specific capabilities]
- Cannot: [list explicit limitations]
- Requires: [dependencies and prerequisites]

ROLE:
- Identity: [who/what the Assistant is]
- Serves: [target audience]
- Purpose: [primary objective]

INPUTS:
- Required: [mandatory fields and formats]
- Optional: [nice-to-have inputs]
- Sources: [specific RAG/IP Vault documents by name]

STEPS:
1. [Step name]: [detailed action]
   - Checkpoint: [verification criteria]
2. [Step name]: [detailed action]
   - Checkpoint: [verification criteria]
[Continue for all steps]

POLICIES:
- Privacy: [data handling rules]
- Safety: [content boundaries]
- Compliance: [regulatory requirements]
- Errors: [handling strategies]

EVALUATION:
- Success criteria: [measurable outcomes]
- Quality metrics: [standards to meet]
- Acceptance tests: [validation steps]

Benefits of CRISPE Design

Comprehensive

Covers all aspects of Assistant behavior and operation systematically.

Auditable

Creates clear documentation of what the Assistant should do and how.

Maintainable

Makes it easy to update and refine Assistant behavior over time.

Reliable

Reduces edge cases and unexpected behaviors through thorough design.

Learn more about building Assistants with CRISPE: Building Assistants with CRISPE

Start with the basic template and evolve to CRISPE as your Assistants become more complex or mission-critical.

9 Iterate and validate with a tight loop

Effective use of Pria requires an iterative approach where you continuously refine your prompts and validate outputs using Tool Details and other feedback mechanisms.

The Iteration Loop

Initial request

Start with a clear, specific prompt based on best practices.

Review output

Evaluate the response for accuracy, completeness, and alignment with your needs.

Check Tool Details

Examine which sources were used, which tools executed, and whether all steps succeeded.

Identify gaps

Determine what’s missing, incorrect, or could be improved.

Refine and retry

Adjust your prompt, add sources, or clarify requirements based on findings.

Repeat until satisfied

Continue the loop until outputs meet your acceptance criteria.

Validation Request Template

"Run with sources A/B. In Tool Details, show: (1) retrieved passages,
(2) any inference steps, (3) unresolved items. If missing, request the exact inputs needed."

What to Look For

Source validation

Were the right documents consulted?
Are there missing sources that should be included?
Is the source information current and accurate?

Completeness

Are all requested elements present?
Is the depth appropriate for your needs?
Are there gaps in coverage or reasoning?

Accuracy

Are facts verifiable and correctly cited?
Is there a clear distinction between retrieved facts and inferences?
Do numbers, dates, and specific details check out?

Quality

Does the tone match your requirements?
Is the structure logical and easy to follow?
Does it meet your stated success criteria?

When iteration reveals issues:

Add specificity

Make your requirements more precise and bounded.

Adjust sources

Add, remove, or replace reference documents.

Break down tasks

Split complex requests into smaller, sequential steps.

Clarify constraints

Make explicit what should and shouldn’t be included.

Treat outputs as drafts until Tool Details confirms the right sources were used and success criteria are met. This iterative validation ensures high-quality, reliable results.

Keep a log of what refinements worked well for different types of tasks. This builds your personal library of effective prompt patterns.

10 Governance, privacy, and safety

Operating Pria responsibly requires attention to data governance, privacy protection, and safety considerations. These practices protect both you and your organization while ensuring compliant, ethical AI use.

Core Governance Principles

Prefer structured sources

Use IP Vault documents and RAG files instead of raw copy-paste or unvetted links. Structured sources provide:

Better traceability and audit trails
Controlled access and versioning
Cleaner, more reliable data
Easier compliance with data policies

Respect data boundaries

Follow your organization’s privacy and safety policies:

Restrict sensitive content appropriately
Verify recipients for any exports
Use proper classification labels
Follow data retention policies
Respect geographic and regulatory boundaries

Human-in-the-loop

Maintain human review for outputs impacting:

Critical business decisions
Grades or evaluations
Compliance or legal matters
Policy creation or changes
Public communications
Financial transactions

AI assistance should augment, not replace, human judgment in high-stakes scenarios.

Transparency and disclosure

Be transparent about AI usage:

Disclose when content is AI-generated
Cite sources appropriately
Explain AI’s role in decision-making
Document processes for auditability

Data Privacy Best Practices

Classify before sharing

Understand the sensitivity level of data before using it with Pria.

Use IP Vault for control

Store sensitive documents in IP Vault where access can be managed and tracked.

Minimize PII exposure

Redact or generalize personally identifiable information when possible.

Review before distribution

Check outputs for inadvertent disclosure of sensitive information.

Safety Considerations

Safety encompasses both preventing harmful outputs and ensuring appropriate use of AI capabilities.

Content safety:

Review outputs for bias, fairness, and appropriateness
Ensure compliance with content policies
Validate factual claims, especially for sensitive topics
Consider potential misuse or misinterpretation

Operational safety:

Test Assistants thoroughly before production use
Implement fallbacks for critical workflows
Monitor for unexpected behaviors or errors
Have contingency plans for AI unavailability

Compliance Framework

Different industries and regions have specific requirements:

Education: FERPA, COPPA, student privacy laws
Healthcare: HIPAA, patient confidentiality
Finance: SOX, PCI-DSS, financial regulations
Europe: GDPR and data protection rules
Government: FedRAMP, ITAR, specific agency requirements

For institutional commitments and protections, see your Praxis AI Data Processing Agreement (DPA) and internal governance policies.

Ethical AI Use

Beyond compliance, consider ethical dimensions:

Fairness: Avoid perpetuating biases or discrimination
Accountability: Take responsibility for AI-assisted outputs
Transparency: Be clear about AI’s role and limitations
Beneficence: Use AI to create positive outcomes
Autonomy: Respect user agency and informed consent

When in doubt about governance, privacy, or safety, consult your organization’s AI governance team or compliance officer before proceeding.

Putting it all together

Mastering Pria requires integrating all these practices into a cohesive workflow. Here’s how to combine them effectively:

The Complete Workflow

Start a correctly named conversation

Set a clear perspective

Select precise files from IP Vault

State objective, constraints, and success criteria

Run

Inspect Tool Details

Refine inputs or steps

Repeat until acceptance tests pass

Templatize into an Assistant with CRISPE

Add fallback for edge cases

Continuous Improvement

As you use Pria more:

Build a prompt library: Save successful prompts and patterns
Refine Assistants: Update based on real-world performance
Document learnings: Note what works and what doesn’t
Share knowledge: Help others learn from your experience
Stay current: Follow Praxis AI updates and new capabilities

Troubleshooting Common Issues

Generic outputs

Solution: Raise specificity (objective, structure, constraints) and narrow sources (IP Vault/RAG).

Incomplete results

Solution: Check Tool Details for missing or failing steps and adjust accordingly.

Wrong context

Solution: Verify you’re in the correct conversation and the right sources are referenced.

Inconsistent quality

Solution: Create an Assistant to standardize the workflow and ensure repeatability.

If results feel generic, raise specificity (objective, structure, constraints) and narrow sources (IP Vault/RAG). If results feel incomplete, check Tool Details for missing or failing steps and adjust accordingly.

Success Indicators

You’re mastering Pria when:

First attempts consistently produce usable outputs
You can predict which sources and steps Pria will use
Assistants handle 80%+ of routine workflows
Tool Details shows successful execution with minimal inference
Iteration cycles are shorter and more targeted
Outputs require less manual editing
Colleagues ask you for tips and templates

Explore these resources to deepen your Praxis AI expertise:

Getting Started: User Guide - Foundation for using Praxis AI
Building Assistants: Assistants Personalization - Advanced Assistant design with CRISPE
IP Vault: Managing Your IP Vault - Learn how to effectively scrape and manage website content with Pria’s RAG
Prompt Engineering: Assistants Overview - Master the art of crafting effective prompts and building custom assistants
Tools: Tools Reference - Understand the capabilities of the AI tools available in Pria

Bookmark this guide and refer back to it regularly as you develop your Praxis AI skills. Mastery comes through consistent application of these practices.

The Gallery Access your LMS

​At a glance

1- Be specific

2- Use IP Vault for websites

3- Verify with Tool Details

4- Manage conversations

5- Set perspective

6- Create Assistants

7- Apply CRISPE

8- Iterate & validate

9- Stay organized

10- Respect governance

​1 Be Specific: The power of detailed requests and context

​Why Specificity Matters

​Building Specific Prompts

​Example: Generic vs. Specific

​Complete Example Prompt

​2 Website research via IP Vault: Comprehensive scraping

​Why IP Vault?

Full-context capture

Clean data

Repeatable

​The Problem with Direct URL Reading

​Using IP Vault Effectively

​Example Usage

​3 Tool Details: Unveil process and verification

​What Tool Details Reveals

​Why This Matters

​4 Manage conversations: Organize context and history

​Why Conversation Management Matters

​Contextual Integrity

​Best Practices for Conversation Management

​Organization and Retrieval

​Assistant-Driven Workflows

​5 Use perspective: Define Pria’s role explicitly

​Why Perspective Matters

​Examples of Effective Perspective Setting

Social Media Expert

AI Engineer

Business Analyst

Technical Writer

​Tailored Communication

​Setting Effective Perspectives

​6 Create Assistants: Automate and optimize workflows

​Why Create Assistants?

Automation

Consistency

Repeatability

Optimization

​Assistant Core Pattern

​Basic Assistant Template

​Key Elements of Effective Assistants

​Advanced Considerations

​7 Error handling patterns for Assistants

​Core Error Handling Patterns

Validate inputs

Constrain sources

Plan-then-execute

Degrade gracefully

​Error Categories to Handle

​Example Error Handling Implementation

​8 Advanced design with CRISPE

​Why CRISPE?

​The CRISPE Framework

​CRISPE Template Structure

​Benefits of CRISPE Design

Comprehensive

Auditable

Maintainable

Reliable

​9 Iterate and validate with a tight loop

​The Iteration Loop

​Validation Request Template

​What to Look For

​Refinement Strategies

Add specificity

Adjust sources

Break down tasks

Clarify constraints

​10 Governance, privacy, and safety

​Core Governance Principles

At a glance

1 Be Specific: The power of detailed requests and context

Why Specificity Matters

Building Specific Prompts

Example: Generic vs. Specific

Complete Example Prompt

2 Website research via IP Vault: Comprehensive scraping

Why IP Vault?

The Problem with Direct URL Reading

Using IP Vault Effectively

Example Usage

3 Tool Details: Unveil process and verification

What Tool Details Reveals

Why This Matters

4 Manage conversations: Organize context and history

Why Conversation Management Matters

Contextual Integrity

Best Practices for Conversation Management

Organization and Retrieval

Assistant-Driven Workflows

5 Use perspective: Define Pria’s role explicitly

Why Perspective Matters

Examples of Effective Perspective Setting

Tailored Communication

Setting Effective Perspectives

6 Create Assistants: Automate and optimize workflows

Why Create Assistants?

Assistant Core Pattern

Basic Assistant Template

Key Elements of Effective Assistants

Advanced Considerations

7 Error handling patterns for Assistants

Core Error Handling Patterns

Error Categories to Handle

Example Error Handling Implementation

8 Advanced design with CRISPE

Why CRISPE?

The CRISPE Framework

CRISPE Template Structure

Benefits of CRISPE Design

9 Iterate and validate with a tight loop

The Iteration Loop

Validation Request Template

What to Look For

Refinement Strategies

10 Governance, privacy, and safety

Core Governance Principles

Data Privacy Best Practices

Safety Considerations

Compliance Framework

Ethical AI Use

Putting it all together

The Complete Workflow

Continuous Improvement

Troubleshooting Common Issues

Success Indicators

Related documentation