Veritas Prompt Management

Overview

This runbook covers operational procedures for managing the Veritas prompt library using SO1 prompt management agents. Veritas is the centralized prompt repository that powers all SO1 agents, ensuring consistency, quality, and versioning of AI prompts across the platform. Purpose: Provide step-by-step instructions for creating, refining, and managing prompts in Veritas Scope: Prompt engineering, chain architecture, fragment management, version control, quality assurance Target Audience: Prompt engineers, AI engineers, agent developers

Prerequisites

Required Access

Veritas prompt library access (GitHub: so1-io/veritas)
Control Plane API access (CONTROL_PLANE_API_KEY)
OpenAI API access (for prompt testing)
Agent execution permissions

Required Tools

OpenCode with prompt management agents installed
Git CLI (for Veritas repository management)
curl or API client
JSON/YAML editor
Prompt testing framework

Required Knowledge

Understanding of prompt engineering principles
Familiarity with chain-of-thought prompting
Knowledge of SO1 agent architecture
Basic understanding of LLM capabilities and limitations

Procedure 1: Refine Agent Prompt

Common triggers for prompt refinement:

Agent producing inconsistent outputs
Agent missing edge cases
Agent not following instructions
Agent verbosity issues (too long/short responses)
New requirements or capabilities added

# Get current agent prompt
curl -s https://veritas.so1.io/api/v1/prompts/agents/workflow-architect \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" | jq '.'

Example Current Prompt:

{
  "prompt_id": "prompt_workflow_architect_v1.2",
  "agent": "workflow-architect",
  "version": "1.2.0",
  "content": "You are an expert n8n workflow architect. Design efficient automation workflows...",
  "fragments": ["system_instructions", "workflow_best_practices", "output_format"],
  "performance": {
    "success_rate": 0.87,
    "avg_response_time_ms": 3200,
    "user_satisfaction": 4.1
  }
}

Step 2: Invoke Prompt Refiner Agent

curl -X POST https://control-plane.so1.io/api/v1/agents/prompt-refiner/refine \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt_id": "prompt_workflow_architect_v1.2",
    "refinement_goals": [
      "improve_consistency",
      "reduce_verbosity",
      "better_error_handling"
    ],
    "test_cases": [
      {
        "input": "Design a webhook processing workflow",
        "expected_output_characteristics": [
          "includes error handling nodes",
          "response under 2000 characters",
          "follows n8n best practices"
        ]
      },
      {
        "input": "Create a scheduled data sync workflow",
        "expected_output_characteristics": [
          "includes retry logic",
          "optimized for performance",
          "proper cron expression"
        ]
      }
    ],
    "constraints": {
      "max_tokens": 4000,
      "temperature": 0.7,
      "preserve_tone": "professional_technical"
    }
  }' | jq '.'

Expected Response:

{
  "refinement_id": "ref_5Xy9mPqRs",
  "status": "completed",
  "original_prompt_id": "prompt_workflow_architect_v1.2",
  "refined_prompt_id": "prompt_workflow_architect_v1.3",
  "changes": [
    {
      "type": "addition",
      "section": "instructions",
      "content": "Always include error handling nodes with retry logic."
    },
    {
      "type": "modification",
      "section": "output_format",
      "before": "Provide detailed explanations for each node.",
      "after": "Provide concise explanations (1-2 sentences per node)."
    },
    {
      "type": "addition",
      "section": "constraints",
      "content": "Keep total response under 2000 characters."
    }
  ],
  "test_results": {
    "test_cases_passed": 2,
    "test_cases_failed": 0,
    "improvements": {
      "consistency_score": "+0.15",
      "avg_response_length": "-35%",
      "error_handling_coverage": "+100%"
    }
  },
  "recommendation": "deploy_to_staging"
}

Step 3: Review Refined Prompt

# Get refined prompt details
curl -s https://control-plane.so1.io/api/v1/agents/prompt-refiner/refinements/ref_5Xy9mPqRs \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" | jq '.refined_prompt'

# Compare with original
curl -X POST https://control-plane.so1.io/api/v1/agents/prompt-refiner/compare \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -d '{
    "prompt_a": "prompt_workflow_architect_v1.2",
    "prompt_b": "prompt_workflow_architect_v1.3",
    "output_format": "diff"
  }' | jq -r '.diff'

Step 4: Test Refined Prompt

# Run A/B test with 100 sample inputs
curl -X POST https://control-plane.so1.io/api/v1/agents/prompt-refiner/ab-test \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt_a": "prompt_workflow_architect_v1.2",
    "prompt_b": "prompt_workflow_architect_v1.3",
    "sample_size": 100,
    "metrics": [
      "response_quality",
      "consistency",
      "execution_time",
      "token_usage"
    ]
  }' | jq '.'

A/B Test Results:

{
  "test_id": "ab_7TnVw9Kj2m",
  "results": {
    "prompt_a": {
      "avg_quality_score": 4.1,
      "consistency": 0.87,
      "avg_exec_time_ms": 3200,
      "avg_tokens": 1850
    },
    "prompt_b": {
      "avg_quality_score": 4.4,
      "consistency": 0.94,
      "avg_exec_time_ms": 2800,
      "avg_tokens": 1200
    }
  },
  "winner": "prompt_b",
  "confidence": 0.95,
  "recommendation": "deploy_to_production"
}

Step 5: Deploy Refined Prompt

# Promote to production in Veritas
curl -X POST https://veritas.so1.io/api/v1/prompts/promote \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt_id": "prompt_workflow_architect_v1.3",
    "environment": "production",
    "rollout_strategy": "gradual",
    "rollout_percentage": 10
  }'

# Monitor performance
watch -n 60 'curl -s https://veritas.so1.io/api/v1/prompts/prompt_workflow_architect_v1.3/metrics \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" | jq ".success_rate, .avg_response_time_ms"'

# Increase rollout if metrics are good
curl -X PATCH https://veritas.so1.io/api/v1/prompts/prompt_workflow_architect_v1.3/rollout \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -d '{"percentage": 50}'

# Full rollout after 24 hours of successful operation
curl -X PATCH https://veritas.so1.io/api/v1/prompts/prompt_workflow_architect_v1.3/rollout \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -d '{"percentage": 100}'

Procedure 2: Design Prompt Chain

Step 1: Define Chain Requirements

Identify when a chain is needed:

Complex task requiring multiple reasoning steps
Need for specialized sub-tasks
Output from one prompt feeds into another
Quality gating (verification/validation steps)

interface ChainSpec {
  name: string;
  purpose: string;
  steps: {
    name: string;
    prompt_template: string;
    inputs: string[];
    outputs: string[];
  }[];
  flow_control: 'sequential' | 'parallel' | 'conditional';
}

Step 2: Invoke Chain Architect Agent

curl -X POST https://control-plane.so1.io/api/v1/agents/chain-architect/design \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "chain_name": "api_endpoint_generation",
    "purpose": "Generate complete API endpoint with validation, implementation, and tests",
    "steps": [
      {
        "name": "design_api_spec",
        "description": "Design API specification with request/response schemas",
        "inputs": ["user_requirements"],
        "outputs": ["api_specification"]
      },
      {
        "name": "generate_implementation",
        "description": "Generate Hono route handler with validation",
        "inputs": ["api_specification"],
        "outputs": ["route_code", "validation_schemas"]
      },
      {
        "name": "generate_tests",
        "description": "Generate test suite for endpoint",
        "inputs": ["api_specification", "route_code"],
        "outputs": ["test_code"]
      },
      {
        "name": "review_quality",
        "description": "Review generated code for quality and best practices",
        "inputs": ["route_code", "validation_schemas", "test_code"],
        "outputs": ["quality_report", "recommendations"]
      }
    ],
    "flow_control": "sequential",
    "error_handling": "retry_with_feedback"
  }' | jq '.'

Expected Response:

{
  "chain_id": "chain_api_endpoint_gen_v1",
  "structure": {
    "steps": [
      {
        "step_id": "step_1_design",
        "prompt_id": "prompt_api_spec_designer_v1.0",
        "type": "generative",
        "retry_policy": {
          "max_retries": 3,
          "backoff": "exponential"
        }
      },
      {
        "step_id": "step_2_implement",
        "prompt_id": "prompt_hono_code_gen_v2.1",
        "type": "generative",
        "depends_on": ["step_1_design"]
      },
      {
        "step_id": "step_3_test",
        "prompt_id": "prompt_test_generator_v1.5",
        "type": "generative",
        "depends_on": ["step_1_design", "step_2_implement"]
      },
      {
        "step_id": "step_4_review",
        "prompt_id": "prompt_code_reviewer_v1.0",
        "type": "validative",
        "depends_on": ["step_2_implement", "step_3_test"],
        "quality_gate": {
          "min_score": 0.85,
          "retry_on_fail": true
        }
      }
    ],
    "execution_strategy": "sequential_with_gates"
  },
  "estimated_tokens": 8500,
  "estimated_time_ms": 12000
}

Step 3: Test Chain Execution

# Execute chain with sample input
curl -X POST https://control-plane.so1.io/api/v1/chains/chain_api_endpoint_gen_v1/execute \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {
      "user_requirements": "Create GET /api/v1/users/:id endpoint that returns user details with authentication"
    },
    "config": {
      "environment": "staging",
      "collect_metrics": true
    }
  }' | jq '.'

Chain Execution Result:

{
  "execution_id": "exec_2mP7nQzXy",
  "status": "completed",
  "steps_completed": 4,
  "steps_failed": 0,
  "total_tokens": 8234,
  "total_time_ms": 11750,
  "outputs": {
    "api_specification": {
      "path": "/api/v1/users/:id",
      "method": "GET",
      "authentication": "bearer_token",
      "response_schema": "{ id, name, email, created_at }"
    },
    "route_code": "// Hono route implementation...",
    "validation_schemas": "// Zod schemas...",
    "test_code": "// Vitest test suite...",
    "quality_report": {
      "score": 0.92,
      "passed_checks": 15,
      "failed_checks": 0,
      "recommendations": ["Consider adding rate limiting"]
    }
  }
}

Step 4: Deploy Chain to Veritas

# Register chain in Veritas
curl -X POST https://veritas.so1.io/api/v1/chains \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "chain_id": "chain_api_endpoint_gen_v1",
    "name": "API Endpoint Generation Chain",
    "description": "Complete API endpoint generation with validation and tests",
    "steps": [...],
    "version": "1.0.0",
    "tags": ["api", "code_generation", "hono"]
  }'

# Make available to agents
curl -X POST https://control-plane.so1.io/api/v1/agents/hono-backend/config \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -d '{
    "add_chains": ["chain_api_endpoint_gen_v1"]
  }'

Procedure 3: Manage Prompt Fragments

Step 1: Identify Reusable Prompt Components

Common fragment types:

System instructions: Role definitions, general behavior
Best practices: Domain-specific guidelines
Output formats: JSON schemas, markdown templates
Error handling: How to handle edge cases
Constraints: Token limits, formatting rules

# List existing fragments
curl -s https://veritas.so1.io/api/v1/fragments \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  | jq '.fragments[] | {id, name, usage_count}'

Step 2: Create New Fragment with Fragment Curator

curl -X POST https://control-plane.so1.io/api/v1/agents/fragment-curator/create \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "fragment_name": "error_handling_best_practices",
    "category": "best_practices",
    "content": "When generating code:\n1. Always include try-catch blocks for async operations\n2. Use proper error types (never throw strings)\n3. Include descriptive error messages\n4. Log errors with appropriate severity levels\n5. Return user-friendly error responses",
    "target_agents": ["hono-backend", "nextjs-frontend", "typescript-shared"],
    "version": "1.0.0",
    "metadata": {
      "author": "prompt-engineering-team",
      "created_date": "2026-03-10",
      "review_status": "approved"
    }
  }' | jq '.'

Expected Response:

{
  "fragment_id": "fragment_error_handling_bp_v1",
  "status": "created",
  "validation": {
    "grammar_check": "passed",
    "clarity_score": 0.94,
    "specificity_score": 0.89
  },
  "recommendations": [
    {
      "type": "enhancement",
      "suggestion": "Consider adding language-specific examples for TypeScript"
    }
  ]
}

Step 3: Test Fragment Impact

# A/B test prompts with and without fragment
curl -X POST https://control-plane.so1.io/api/v1/agents/fragment-curator/test-impact \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "fragment_id": "fragment_error_handling_bp_v1",
    "test_agents": ["hono-backend"],
    "sample_size": 50,
    "metrics": ["error_handling_coverage", "code_quality", "user_satisfaction"]
  }' | jq '.'

Step 4: Deploy Fragment

# Add fragment to target agent prompts
curl -X POST https://veritas.so1.io/api/v1/fragments/fragment_error_handling_bp_v1/deploy \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "agents": ["hono-backend", "nextjs-frontend", "typescript-shared"],
    "position": "before_output_format",
    "required": true
  }'

# Verify fragment is active
curl -s https://veritas.so1.io/api/v1/prompts/agents/hono-backend \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  | jq '.fragments'

Step 5: Monitor Fragment Performance

# Get fragment usage analytics
curl -s https://veritas.so1.io/api/v1/fragments/fragment_error_handling_bp_v1/analytics \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  | jq '{
    usage_count: .usage_count,
    avg_impact_on_quality: .metrics.avg_quality_improvement,
    agents_using: .agents_using
  }'

Procedure 4: Version Control and Rollback

Step 1: Tag Prompt Version

# Create version tag for prompt
curl -X POST https://veritas.so1.io/api/v1/prompts/prompt_workflow_architect_v1.3/tag \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "tag": "stable",
    "notes": "Improved consistency and reduced verbosity. Tested with 100 samples.",
    "changelog": [
      "Added error handling requirements",
      "Reduced verbosity in output format",
      "Added response length constraints"
    ]
  }'

Step 2: Monitor Production Performance

# Set up alerts for prompt performance degradation
curl -X POST https://veritas.so1.io/api/v1/alerts \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt_id": "prompt_workflow_architect_v1.3",
    "metrics": [
      {
        "metric": "success_rate",
        "threshold": 0.90,
        "condition": "below"
      },
      {
        "metric": "avg_response_time_ms",
        "threshold": 5000,
        "condition": "above"
      }
    ],
    "notification_channels": ["slack_engineering", "email_oncall"]
  }'

# Check real-time metrics
curl -s https://veritas.so1.io/api/v1/prompts/prompt_workflow_architect_v1.3/metrics/realtime \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  | jq '{success_rate, avg_response_time_ms, error_count}'

Step 3: Rollback if Needed

When prompt performance degrades:

# Immediate rollback to previous version
curl -X POST https://veritas.so1.io/api/v1/prompts/rollback \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "current_prompt_id": "prompt_workflow_architect_v1.3",
    "target_version": "v1.2",
    "reason": "Success rate dropped to 0.82, below threshold",
    "immediate": true
  }'

# Verify rollback
curl -s https://veritas.so1.io/api/v1/prompts/agents/workflow-architect \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  | jq '.version'

Verification Checklist

After completing prompt management operations, verify:

Troubleshooting

Issue	Symptoms	Root Cause	Resolution
Inconsistent Outputs	Same input produces different results	Temperature too high, ambiguous instructions	Lower temperature (0.3-0.5), add specific constraints
Prompt Too Long	Context window errors, high costs	Excessive verbosity, redundant instructions	Use fragments, remove redundant sections, increase conciseness
Chain Step Failures	Chain execution stops mid-way	Step dependency failure, quality gate not met	Add retry logic, review quality gate thresholds
Fragment Conflicts	Contradictory instructions	Multiple fragments with conflicting guidance	Review fragment order, merge conflicting fragments
Low Success Rate	Agent outputs don’t meet requirements	Unclear instructions, missing edge cases	Add examples, specify edge case handling
High Token Usage	Costs increasing rapidly	Verbose prompts, unnecessary chain steps	Optimize prompt length, remove redundant steps
Slow Response Times	Agent execution takes >10s	Large context, complex reasoning	Simplify prompt, use chain for multi-step reasoning
Rollback Failed	Previous version not available	Version not tagged, deleted	Always tag stable versions, implement retention policy

Detailed Troubleshooting: Inconsistent Outputs

# Analyze output variance
curl -X POST https://control-plane.so1.io/api/v1/agents/prompt-refiner/analyze-variance \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt_id": "prompt_workflow_architect_v1.3",
    "test_input": "Design a webhook processing workflow",
    "runs": 20
  }' | jq '.variance_metrics'

# Common fixes:

# 1. Lower temperature
curl -X PATCH https://veritas.so1.io/api/v1/prompts/prompt_workflow_architect_v1.3 \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -d '{"temperature": 0.3}'

# 2. Add output format constraints
curl -X POST https://control-plane.so1.io/api/v1/agents/prompt-refiner/add-constraint \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -d '{
    "prompt_id": "prompt_workflow_architect_v1.3",
    "constraint": "Always output valid JSON with keys: name, nodes, connections"
  }'

# 3. Use seed for reproducibility (if supported by LLM)
curl -X PATCH https://veritas.so1.io/api/v1/prompts/prompt_workflow_architect_v1.3 \
  -H "Authorization: Bearer ${CONTROL_PLANE_API_KEY}" \
  -d '{"seed": 42}'

Prompt Refiner Agent

Iterative prompt optimization

Chain Architect Agent

Multi-step prompt chain design

Fragment Curator Agent

Reusable prompt component management

Veritas Integration

Veritas architecture and integration patterns

Best Practices

Prompt Engineering

Be specific and clear: Avoid ambiguous language, provide concrete examples
Use structured output formats: JSON schemas, markdown templates
Include constraints: Token limits, response length, formatting rules
Test with edge cases: Empty inputs, very long inputs, malformed data
Version everything: Tag stable versions, maintain changelog

Chain Design

Keep chains focused: Each step should have a single, clear purpose
Add quality gates: Validate outputs before proceeding to next step
Implement error handling: Retry with feedback, fallback strategies
Minimize token usage: Only pass necessary context between steps
Monitor chain performance: Track success rates, execution times per step

Fragment Management

Keep fragments atomic: Single responsibility per fragment
Use descriptive names: Clear, searchable fragment IDs
Document usage: Specify which agents should use each fragment
Test impact: A/B test before deploying to all agents
Regular review: Audit fragments quarterly, remove unused ones

Version Control

Tag all stable versions: Use semantic versioning (major.minor.patch)
Maintain rollback capability: Keep previous 3 versions accessible
Document changes: Clear changelogs for every version
Gradual rollout: Use canary deployments (10% → 50% → 100%)
Monitor after deployment: Alert on performance degradation

Runbooks

Domain Runbooks

Overview

Prerequisites

Procedure 1: Refine Agent Prompt

Step 1: Identify Prompt for Refinement

Step 2: Invoke Prompt Refiner Agent

Step 3: Review Refined Prompt

Step 4: Test Refined Prompt

Step 5: Deploy Refined Prompt

Procedure 2: Design Prompt Chain

Step 1: Define Chain Requirements

Step 2: Invoke Chain Architect Agent

Step 3: Test Chain Execution

Step 4: Deploy Chain to Veritas

Procedure 3: Manage Prompt Fragments

Step 1: Identify Reusable Prompt Components

Step 2: Create New Fragment with Fragment Curator

Step 3: Test Fragment Impact

Step 4: Deploy Fragment

Step 5: Monitor Fragment Performance

Procedure 4: Version Control and Rollback

Step 1: Tag Prompt Version

Step 2: Monitor Production Performance

Step 3: Rollback if Needed

Verification Checklist

Troubleshooting

Detailed Troubleshooting: Inconsistent Outputs

Prompt Refiner Agent

Chain Architect Agent

Fragment Curator Agent

Veritas Integration

Best Practices

Prompt Engineering

Chain Design

Fragment Management

Version Control

Runbooks

Domain Runbooks

​Overview

​Prerequisites

​Procedure 1: Refine Agent Prompt

​Step 1: Identify Prompt for Refinement

​Step 2: Invoke Prompt Refiner Agent

​Step 3: Review Refined Prompt

​Step 4: Test Refined Prompt

​Step 5: Deploy Refined Prompt

​Procedure 2: Design Prompt Chain

​Step 1: Define Chain Requirements

​Step 2: Invoke Chain Architect Agent

​Step 3: Test Chain Execution

​Step 4: Deploy Chain to Veritas

​Procedure 3: Manage Prompt Fragments

​Step 1: Identify Reusable Prompt Components

​Step 2: Create New Fragment with Fragment Curator

​Step 3: Test Fragment Impact

​Step 4: Deploy Fragment

​Step 5: Monitor Fragment Performance

​Procedure 4: Version Control and Rollback

​Step 1: Tag Prompt Version

​Step 2: Monitor Production Performance

​Step 3: Rollback if Needed

​Verification Checklist

​Troubleshooting

​Detailed Troubleshooting: Inconsistent Outputs

​Related Resources

Prompt Refiner Agent

Chain Architect Agent

Fragment Curator Agent

Veritas Integration

​Best Practices

​Prompt Engineering

​Chain Design

​Fragment Management

​Version Control

Overview

Prerequisites

Procedure 1: Refine Agent Prompt

Step 1: Identify Prompt for Refinement

Step 2: Invoke Prompt Refiner Agent

Step 3: Review Refined Prompt

Step 4: Test Refined Prompt

Step 5: Deploy Refined Prompt

Procedure 2: Design Prompt Chain

Step 1: Define Chain Requirements

Step 2: Invoke Chain Architect Agent

Step 3: Test Chain Execution

Step 4: Deploy Chain to Veritas

Procedure 3: Manage Prompt Fragments

Step 1: Identify Reusable Prompt Components

Step 2: Create New Fragment with Fragment Curator

Step 3: Test Fragment Impact

Step 4: Deploy Fragment

Step 5: Monitor Fragment Performance

Procedure 4: Version Control and Rollback

Step 1: Tag Prompt Version

Step 2: Monitor Production Performance

Step 3: Rollback if Needed

Verification Checklist

Troubleshooting

Detailed Troubleshooting: Inconsistent Outputs

Related Resources

Best Practices

Prompt Engineering

Chain Design

Fragment Management

Version Control