Prompt Engineering Framework Specification
Overview
The Prompt Engineering Framework provides a comprehensive system for designing, testing, optimizing, and managing prompts across various AI models and use cases. This framework enables systematic prompt development with version control, A/B testing, performance tracking, and automated optimization.
Core Concepts
1. Prompt Architecture
Prompt Components
prompt_structure:
system_prompt:
purpose: "Defines the AI's role, capabilities, and constraints"
elements:
- role_definition
- capability_boundaries
- output_format_specs
- behavioral_guidelines
context_injection:
purpose: "Provides relevant background information"
elements:
- domain_knowledge
- examples
- reference_data
- state_information
task_instruction:
purpose: "Specifies what the AI should accomplish"
elements:
- clear_objective
- success_criteria
- constraints
- step_by_step_guidance
output_shaping:
purpose: "Controls the format and style of responses"
elements:
- format_templates
- style_guidelines
- length_constraints
- structure_requirements
2. Prompt Types
Base Prompt Types
prompt_types:
zero_shot:
description: "Direct instruction without examples"
use_cases:
- simple_tasks
- general_knowledge
- creative_generation
few_shot:
description: "Instruction with examples"
use_cases:
- pattern_learning
- format_compliance
- style_matching
chain_of_thought:
description: "Step-by-step reasoning prompts"
use_cases:
- complex_reasoning
- mathematical_problems
- analytical_tasks
role_based:
description: "AI assumes specific persona or expertise"
use_cases:
- domain_expertise
- creative_writing
- professional_advice
template_based:
description: "Structured templates with variables"
use_cases:
- repetitive_tasks
- data_processing
- report_generation
Prompt Templates
1. Template Definition System
template:
name: analysis_report_template
version: "2.0.0"
type: template_based
description: "Comprehensive analysis report generation"
metadata:
author: "prompt_engineering_team"
tags: ["analysis", "reporting", "structured"]
performance_score: 0.92
usage_count: 15420
variables:
- name: subject
type: string
required: true
description: "Subject of analysis"
validation:
min_length: 3
max_length: 100
- name: data_points
type: array
required: true
description: "Data to analyze"
validation:
min_items: 1
max_items: 50
- name: analysis_depth
type: string
enum: ["surface", "standard", "deep", "comprehensive"]
default: "standard"
- name: target_audience
type: string
enum: ["technical", "executive", "general"]
default: "general"
template_structure:
system: |
You are an expert analyst specializing in {{ subject }} analysis.
Your role is to provide {{ analysis_depth }} insights that are
appropriate for a {{ target_audience }} audience.
Guidelines:
- Be objective and data-driven
- Support conclusions with evidence
- Highlight key patterns and anomalies
- Provide actionable recommendations
user: |
Analyze the following {{ subject }} data and provide a comprehensive report:
Data Points:
{{ data_points | format_as_list }}
Please structure your analysis as follows:
1. Executive Summary (2-3 sentences)
2. Key Findings (3-5 bullet points)
3. Detailed Analysis
4. Trends and Patterns
5. Recommendations
6. Potential Risks or Concerns
Tailor the language and technical depth for a {{ target_audience }} audience.
post_processing:
- type: format_validation
rules:
- has_all_sections
- section_length_limits
- type: quality_check
criteria:
- clarity_score
- completeness_score
- actionability_score
2. Dynamic Template Generation
dynamic_template:
name: adaptive_prompt_generator
type: dynamic
generation_rules:
- condition: "task_complexity == 'high'"
modifications:
- add_chain_of_thought
- increase_example_count
- add_verification_step
- condition: "model == 'gpt-4' and token_budget > 2000"
modifications:
- use_detailed_instructions
- add_reasoning_framework
- condition: "output_format == 'structured'"
modifications:
- add_json_schema
- include_format_examples
- add_validation_rules
optimization_feedback:
- success_rate
- completion_time
- token_efficiency
- output_quality_score
Prompt Optimization
1. A/B Testing Framework
ab_testing:
test_configuration:
name: "report_generation_optimization"
hypothesis: "Adding step-by-step reasoning improves accuracy"
variants:
control:
prompt_template: "standard_report_template"
description: "Current production prompt"
variant_a:
prompt_template: "report_with_reasoning_template"
description: "Added chain-of-thought reasoning"
modifications:
- add_reasoning_section
- include_thinking_process
variant_b:
prompt_template: "report_with_examples_template"
description: "Added relevant examples"
modifications:
- add_3_examples
- include_counter_examples
test_parameters:
sample_size: 1000
distribution:
control: 0.5
variant_a: 0.25
variant_b: 0.25
duration: "7 days"
success_metrics:
primary:
- accuracy_score
- user_satisfaction
secondary:
- completion_time
- token_usage
- error_rate
statistical_analysis:
confidence_level: 0.95
minimum_detectable_effect: 0.05
early_stopping_rules:
- significant_degradation
- clear_winner
2. Automated Optimization
automated_optimization:
optimizer_config:
name: "prompt_evolution_engine"
type: "genetic_algorithm"
optimization_targets:
- metric: accuracy
weight: 0.4
threshold: 0.9
- metric: token_efficiency
weight: 0.3
direction: minimize
- metric: latency
weight: 0.2
threshold: 2000ms
- metric: consistency
weight: 0.1
threshold: 0.85
evolution_parameters:
population_size: 50
generations: 20
mutation_rate: 0.1
crossover_rate: 0.7
selection_method: "tournament"
mutation_operators:
- type: instruction_rephrasing
probability: 0.3
methods:
- synonym_replacement
- sentence_restructuring
- detail_adjustment
- type: example_modification
probability: 0.2
methods:
- example_replacement
- example_addition
- example_removal
- type: structure_alteration
probability: 0.2
methods:
- section_reordering
- section_merging
- section_splitting
- type: context_tuning
probability: 0.3
methods:
- context_expansion
- context_compression
- context_clarification
fitness_evaluation:
test_set_size: 100
evaluation_runs: 3
aggregation_method: "weighted_average"
convergence_criteria:
- no_improvement_generations: 5
- target_fitness_achieved: 0.95
- maximum_generations: 20
3. Prompt Compression
compression_strategy:
name: "intelligent_prompt_compression"
compression_techniques:
- name: redundancy_removal
description: "Remove duplicate instructions or context"
average_reduction: "15-20%"
quality_impact: "minimal"
- name: instruction_consolidation
description: "Combine related instructions"
average_reduction: "10-15%"
quality_impact: "minimal"
- name: example_optimization
description: "Use most representative examples"
average_reduction: "20-30%"
quality_impact: "low"
- name: context_summarization
description: "Summarize verbose context"
average_reduction: "30-40%"
quality_impact: "medium"
compression_pipeline:
- step: analyze_prompt_structure
identifies:
- redundant_sections
- verbose_instructions
- unnecessary_examples
- step: apply_compression
methods:
- semantic_deduplication
- instruction_merging
- example_selection
- context_distillation
- step: quality_validation
checks:
- semantic_preservation
- instruction_completeness
- performance_maintenance
- step: iterative_refinement
process:
- test_compressed_version
- measure_performance_delta
- adjust_compression_level
Prompt Library Management
1. Version Control System
version_control:
repository_structure:
prompts/
production/
- current_version.yaml
- metadata.json
staging/
- candidate_versions/
- test_results/
archive/
- deprecated_versions/
- migration_guides/
versioning_scheme:
format: "MAJOR.MINOR.PATCH"
change_types:
major:
- structural_changes
- breaking_modifications
- significant_behavior_changes
minor:
- new_capabilities
- performance_improvements
- non_breaking_enhancements
patch:
- bug_fixes
- typo_corrections
- minor_adjustments
change_tracking:
required_fields:
- change_description
- impact_assessment
- testing_results
- rollback_plan
approval_workflow:
- developer_submission
- automated_testing
- peer_review
- performance_validation
- production_approval
2. Prompt Registry
prompt_registry:
catalog_structure:
categories:
- name: text_generation
subcategories:
- creative_writing
- technical_documentation
- marketing_content
- academic_writing
- name: analysis
subcategories:
- data_analysis
- sentiment_analysis
- code_review
- business_intelligence
- name: conversation
subcategories:
- customer_support
- tutoring
- counseling
- sales
- name: task_automation
subcategories:
- data_extraction
- summarization
- translation
- classification
metadata_schema:
required:
- name
- version
- description
- category
- supported_models
- performance_metrics
- usage_examples
optional:
- prerequisites
- limitations
- best_practices
- troubleshooting
- related_prompts
search_capabilities:
- keyword_search
- category_browsing
- performance_filtering
- model_compatibility
- similarity_search
Prompt Performance Analytics
1. Metrics Collection
metrics_framework:
core_metrics:
quality_metrics:
- accuracy_score:
description: "Correctness of outputs"
measurement: "human_evaluation | automated_scoring"
range: [0, 1]
- relevance_score:
description: "Output relevance to task"
measurement: "embedding_similarity | human_rating"
range: [0, 1]
- coherence_score:
description: "Logical consistency"
measurement: "nlp_analysis | human_evaluation"
range: [0, 1]
- completeness_score:
description: "Task completion level"
measurement: "checklist_validation | coverage_analysis"
range: [0, 1]
efficiency_metrics:
- token_usage:
description: "Total tokens consumed"
measurement: "api_reported"
optimization_target: "minimize"
- response_time:
description: "Time to generate response"
measurement: "end_to_end_latency"
optimization_target: "minimize"
- cost_per_request:
description: "Financial cost"
measurement: "token_usage * price_per_token"
optimization_target: "minimize"
reliability_metrics:
- success_rate:
description: "Successful completions"
measurement: "completed / total_attempts"
range: [0, 1]
- error_rate:
description: "Failed generations"
measurement: "errors / total_attempts"
range: [0, 1]
- consistency_score:
description: "Output consistency"
measurement: "variance_analysis"
range: [0, 1]
collection_pipeline:
- stage: execution_monitoring
collects:
- request_parameters
- response_data
- timing_information
- error_details
- stage: quality_assessment
processes:
- automated_scoring
- sample_human_review
- comparative_analysis
- stage: aggregation
generates:
- performance_reports
- trend_analysis
- anomaly_detection
2. Performance Dashboard
analytics_dashboard:
real_time_monitoring:
widgets:
- prompt_usage_heatmap
- error_rate_timeline
- token_usage_gauge
- response_time_histogram
- quality_score_trends
comparative_analysis:
views:
- prompt_version_comparison
- model_performance_matrix
- cost_efficiency_analysis
- user_satisfaction_correlation
optimization_insights:
recommendations:
- underperforming_prompts
- optimization_opportunities
- cost_reduction_suggestions
- quality_improvement_areas
alerting_rules:
- metric: error_rate
threshold: 0.05
action: immediate_notification
- metric: quality_score
threshold: 0.8
condition: "below"
action: review_required
- metric: cost_per_request
threshold: "$0.10"
action: cost_alert
Advanced Features
1. Multi-Model Optimization
multi_model_optimization:
supported_models:
- provider: openai
models: ["gpt-4", "gpt-3.5-turbo"]
- provider: anthropic
models: ["claude-2", "claude-instant"]
- provider: google
models: ["palm-2", "gemini-pro"]
- provider: open_source
models: ["llama-2", "mistral", "falcon"]
optimization_strategy:
approach: "model_specific_tuning"
techniques:
- prompt_adaptation:
description: "Adjust prompt for model capabilities"
customizations:
- instruction_style
- example_format
- context_structure
- performance_profiling:
description: "Profile each model's strengths"
metrics:
- task_accuracy
- response_speed
- cost_efficiency
- capability_coverage
- dynamic_routing:
description: "Route requests to optimal model"
factors:
- task_complexity
- required_capabilities
- budget_constraints
- latency_requirements
2. Prompt Chaining
prompt_chaining:
chain_definition:
name: "comprehensive_analysis_chain"
description: "Multi-stage analysis with refinement"
stages:
- stage: initial_analysis
prompt_template: "quick_analysis_template"
purpose: "Generate initial insights"
outputs:
- preliminary_findings
- areas_for_deep_dive
- stage: deep_analysis
prompt_template: "detailed_investigation_template"
inputs:
- previous_output: preliminary_findings
- focus_areas: areas_for_deep_dive
purpose: "Detailed investigation of key areas"
outputs:
- detailed_findings
- supporting_evidence
- stage: synthesis
prompt_template: "synthesis_template"
inputs:
- all_findings: [preliminary_findings, detailed_findings]
- evidence: supporting_evidence
purpose: "Synthesize comprehensive report"
outputs:
- final_report
- executive_summary
- stage: quality_check
prompt_template: "review_template"
inputs:
- report: final_report
purpose: "Verify quality and completeness"
outputs:
- quality_score
- improvement_suggestions
error_handling:
retry_strategy: "exponential_backoff"
fallback_behavior: "use_previous_stage_output"
max_retries: 3
optimization:
parallel_execution: true
cache_intermediate_results: true
total_timeout: 120s
3. Prompt Security
security_framework:
injection_prevention:
sanitization_rules:
- remove_system_commands
- escape_special_characters
- validate_input_format
- limit_input_length
detection_patterns:
- prompt_injection_attempts
- jailbreak_patterns
- malicious_instructions
- data_exfiltration_attempts
output_filtering:
content_filters:
- pii_detection:
patterns: ["ssn", "credit_card", "email", "phone"]
action: "redact"
- inappropriate_content:
categories: ["violence", "hate_speech", "adult_content"]
action: "block"
- confidential_information:
patterns: ["api_keys", "passwords", "internal_data"]
action: "remove"
access_control:
prompt_permissions:
- role_based_access
- usage_quotas
- model_restrictions
- data_access_limits
audit_logging:
- user_actions
- prompt_modifications
- access_attempts
- security_violations
Testing Framework
1. Prompt Testing Suite
testing_framework:
test_types:
unit_tests:
description: "Test individual prompt components"
coverage:
- variable_substitution
- format_compliance
- instruction_clarity
- example_validity
integration_tests:
description: "Test prompt with actual models"
coverage:
- model_compatibility
- response_quality
- error_handling
- performance_metrics
regression_tests:
description: "Ensure changes don't degrade performance"
coverage:
- baseline_comparison
- quality_maintenance
- behavior_consistency
- edge_case_handling
stress_tests:
description: "Test under extreme conditions"
coverage:
- large_inputs
- rapid_requests
- edge_cases
- adversarial_inputs
test_data_generation:
strategies:
- representative_sampling
- edge_case_generation
- adversarial_examples
- synthetic_data_creation
quality_assurance:
automated_checks:
- grammar_validation
- instruction_completeness
- format_consistency
- performance_benchmarks
human_evaluation:
- expert_review
- user_testing
- a_b_testing
- feedback_collection
2. Continuous Improvement
continuous_improvement:
feedback_loops:
- user_feedback:
collection_methods:
- ratings
- comments
- usage_patterns
- error_reports
analysis:
- sentiment_analysis
- pattern_identification
- priority_scoring
- performance_monitoring:
automated_tracking:
- success_rates
- quality_scores
- efficiency_metrics
- cost_analysis
anomaly_detection:
- performance_degradation
- unusual_patterns
- error_spikes
- model_updates:
adaptation_strategy:
- performance_testing
- prompt_adjustment
- reoptimization
- migration_planning
improvement_pipeline:
- collect_feedback
- analyze_patterns
- generate_hypotheses
- design_experiments
- implement_changes
- measure_impact
- deploy_improvements
Implementation Guidelines
1. Best Practices
best_practices:
prompt_design:
- be_specific_and_clear
- provide_context_appropriately
- use_consistent_formatting
- include_relevant_examples
- specify_output_format
- handle_edge_cases
- test_thoroughly
optimization:
- start_with_baseline
- iterate_systematically
- measure_everything
- document_changes
- maintain_version_history
- automate_testing
- monitor_production
security:
- sanitize_all_inputs
- validate_outputs
- implement_access_controls
- audit_usage
- prevent_injection
- protect_sensitive_data
- regular_security_reviews
2. Migration Strategy
migration_strategy:
phases:
- phase_1_assessment:
tasks:
- inventory_existing_prompts
- evaluate_current_performance
- identify_improvement_areas
- prioritize_migrations
- phase_2_standardization:
tasks:
- convert_to_templates
- add_metadata
- implement_versioning
- create_test_suites
- phase_3_optimization:
tasks:
- baseline_performance
- run_optimization
- validate_improvements
- deploy_gradually
- phase_4_monitoring:
tasks:
- setup_analytics
- implement_alerting
- collect_feedback
- continuous_improvement
Future Enhancements
1. AI-Powered Prompt Generation
- Automatic prompt creation from requirements
- Self-improving prompt systems
- Context-aware prompt adaptation
- Natural language prompt programming
2. Advanced Analytics
- Predictive performance modeling
- Cost optimization algorithms
- Quality prediction systems
- Automated A/B test generation
3. Integration Ecosystem
- IDE plugins for prompt development
- CI/CD pipeline integration
- Monitoring dashboard integrations
- Collaborative prompt development tools
4. Research Applications
- Prompt pattern mining
- Cross-model prompt transfer
- Prompt compression research
- Security vulnerability research