Llama 3.1 8b

Generated Strategic Themes & OKRs

I want to build an integrated solution, that would allow a user to resolve tasks on each step of SAFe with AI (local LLMs). It ... should have a knowledge base (an open-source solution, probably nextra), a ticket system (also open-source, analogous to JIRA ... ), access to operating system and file system (to be able to CRUD of files, write code, etc). It might use multiple different ... LLMs for different tasks depending on the domain. Create a set of strategic Themes, OKRs, that would reflect my idea.

Here's a possible set of Strategic Themes and OKRs that could guide the development of your integrated solution:

Strategic Themes:

1. "Integrate AI-Powered Task Resolution": This theme focuses on developing an intelligent task resolution system that leverages local LLMs to assist users in completing tasks at each step of the SAFe framework.
2. "Create a Knowledge Base for Domain Expertise": This theme emphasizes building a comprehensive knowledge base using open-source solutions like Nextra, which will serve as a foundation for domain-specific expertise and task resolution.
3. "Implement an Integrated Ticketing System": This theme involves developing a ticketing system that integrates with the knowledge base and AI-powered task resolution system, mirroring the functionality of JIRA but tailored to your specific use case.
4. "Provide Access to Operating System and File System Resources": This theme focuses on ensuring seamless integration with the operating system and file system, enabling users to perform CRUD operations, write code, and interact with files in a controlled environment.

OKRs:

Objective: "Integrate AI-Powered Task Resolution"

• Key Results:
  • Develop a working prototype of the AI-powered task resolution system using local LLMs within 6 months.
  • Achieve an accuracy rate of at least 80% for task resolution suggestions generated by the system within the next 9 months.

Objective: "Create a Knowledge Base for Domain Expertise"

• Key Results:
  • Integrate Nextra with the ticketing system and AI-powered task resolution system within 3 months.
  • Develop a comprehensive knowledge base covering at least 5 domains, each with a minimum of 500 relevant articles and resources within the next 12 months.

Objective: "Implement an Integrated Ticketing System"

• Key Results:
  • Develop a working prototype of the integrated ticketing system within 4 months.
  • Achieve an average resolution time of less than 2 days for tickets assigned to AI-powered task resolution within the next 9 months.

Objective: "Provide Access to Operating System and File System Resources"

• Key Results:
  • Develop a working prototype of the OS/file system integration within 5 months.
  • Achieve an accuracy rate of at least 90% for file operations (CRUD) performed through the integrated interface within the next 12 months.

Domain-Specific LLMs

To reflect your idea, you might consider additional OKRs that focus on integrating multiple LLMs for different domains. For example:

• Objective: "Integrate LLMs for Software Development"

  • Key Results:
    • Integrate a software development-specific LLM with the AI-powered task resolution system within 6 months.
    • Achieve an accuracy rate of at least 85% for code suggestions generated by the integrated LLM within the next 9 months.

• Objective: "Integrate LLMs for DevOps"

  • Key Results:
    • Integrate a DevOps-specific LLM with the AI-powered task resolution system within 6 months.
    • Achieve an accuracy rate of at least 80% for infrastructure provisioning suggestions generated by the integrated LLM within the next 9 months.

These are just some possible OKRs and Strategic Themes that could guide your project. You may need to adjust or add to them based on your specific requirements and needs.

Generated Portfolio Canvas

Generate a Portfolio Canvas for it. It should have: Value Propositions (value streams, solutions, customers, channels, customer relationships, budget, KPIs/Revenue), Key Partners, Key Activities, Key Resources, ... Cost Structure, Revenue Streams

Here's the completed Portfolio Canvas:

Portfolio Name: Integrated AI-Powered Task Resolution Solution

Value Propositions:

1. Value Stream: Leverage local LLMs to simplify SAFe workflows and provide access to operating system and file system resources.
2. Solutions: AI-powered task resolution suggestions, comprehensive knowledge base for domain expertise, and seamless integration with SAFe workflows.
3. Customers:
   • End-users who benefit from the integrated solution (20%)
   • Businesses that adopt the solution to streamline workflow and improve productivity (30%)
4. Channels:
   • Online platform for user adoption and support
   • In-person training sessions for developers and stakeholders
5. Customer Relationships:
   • Personalized onboarding process for users
   • Regular updates and feedback mechanisms for customers
6. Budget: $1 million (Year 1), increasing to $2 million by Year 3.
7. KPIs/Revenue:
   • Accuracy rate of AI-powered task resolution suggestions (>90%)
   • User adoption rate (>80%)
   • Revenue growth (20% YoY)

Key Partners:

1. LLM Providers: Companies that supply local LLMs for integration with the solution.
2. SAFe Experts: Professionals who provide expertise and support for integrating SAFe workflows.
3. Operating System Vendors: Companies that provide access to operating system resources.
4. File System Providers: Organizations that grant access to file system resources.

Key Activities:

1. Development: Developing the integrated solution, including LLM integration and OS/file system access.
2. Testing & Quality Assurance: Ensuring the quality and accuracy of the AI-powered task resolution suggestions.
3. User Training & Support: Providing personalized onboarding process, regular updates, and feedback mechanisms for customers.
4. Marketing & Sales: Promoting the solution to end-users and businesses, and securing partnerships with LLM providers.

Key Resources:

1. Development Team: Skilled developers who integrate LLMs and develop the solution.
2. LLM Licenses: Licenses granted by LLM providers for integration with the solution.
3. SAFe Expertise: Access to SAFe experts for support and guidance.
4. Operating System & File System Resources: Access to operating system and file system resources.

Cost Structure:

1. Development Costs: $300,000 (Year 1), increasing to $600,000 by Year 3.
2. LLM Licensing Fees: $200,000 (Year 1), increasing to $400,000 by Year 3.
3. Marketing & Sales Expenses: $150,000 (Year 1), increasing to $300,000 by Year 3.

Revenue Streams:

1. Subscription Model: Offer users a subscription-based model for access to the solution and support services.
2. LLM Licensing Fees: Earn revenue from LLM licensing fees paid by customers.
3. Training & Support Services: Provide additional training and support services to customers, generating additional revenue.

This Portfolio Canvas provides a comprehensive overview of the integrated AI-powered task resolution solution, including its value proposition, key partners, activities, resources, cost structure, and revenue streams.

1. Value Propositions The value propositions describe the customers and the value delivered by the solutions of each value stream, as well as the customer segments and relationships, budget, and KPIs /revenue. Create a separate row for each development value stream. Value Streams: The development value streams used to build the systems and capabilities that enable business processes in operational value streams or provide products and services to the operational value streams. Solutions: Each value stream produces one or more solutions, which are the products, services, or systems delivered to the customer, whether internal or external to the enterprise. Customers: Customers describe the internal or external customers for each value stream. It defines how the business views and treats various sets of customers differently based on their common attributes. Channels: Explains how the enterprise delivers its products and services to intermediaries, customers, and end-users. If serving external customers, this may include marketing and sales mechanisms used to reach customers (e.g., web, direct sales, brick-and-mortar store, distribution network). If serving internal customers, it captures the interfaces with internal stakeholders and end-users (for example, internal websites or custom IT applications). Customer Relationships: The types of customer relationships needed to effectively apply and leverage the business’ products and services. It describes the connections and communications with each customer segment. These relationships influence the design of solutions and the allocation of resources within the portfolio. Budget: Each value stream is assigned a Lean Budget, which includes operating, overhead, and capital expenses. KPIs / Revenue: Key Performance Indicators (KPIs) define the measures used to evaluate the results of the value stream investment.

2. Resources and Activities The resources and activities describe the key partners, activities, and other resources needed to achieve the value propositions. Key Partners – The various buyer-supplier relationships and business alliances that facilitate achieving the value proposition. Key Activities – The enterprise’s most important actions to deliver its products and services. Key Resources – The enterprise’s critical physical, intellectual, financial, human, and other capabilities and assets used to achieve its objectives.

3. Cost Structure and Revenue Streams The cost structure and revenue streams describe how the portfolio’s costs are structured and define how revenue or value is achieved. Cost Structure: Identifies the most significant costs in the portfolio’s business model, including the structural aspects, such as license costs, development labs, and costs of external services. Building cyber-physical systems also have other costs (for example, hardware and firmware), which must be considered. Revenue Streams: If the development value streams monetize directly, list the types and sources of revenue from customers. Note the major sources of revenue and how the customer is charged (fixed price, usage-based, and so on). For internal customers, non-profits, and government agencies, describe the value of the solutions.

Portfolio Name: Integrated AI-Powered Task Resolution Solution

Value Propositions:

Resources and Activities:

Cost Structure:

Revenue Streams:

Create for each hierarcy level accord ... ing to Full Portfolio SAFe (levels: Portfolio, Large Solution, Essential) detailed items: Epics & Enablers for Portoflio level, Capabilities & Enablers for Large Solution level, Features, Stories & Enablers for ... Essential level Here are the detailed items for each hierarchy level:

Portfolio Level:

• Epic 1: "Integrate AI-Powered Task Resolution"
  • Description: Integrate local LLMs to assist users in completing tasks at each step of the SAFe framework.
  • Enablers:
    • Develop and deploy cloud infrastructure to support AI-powered task resolution suggestions
    • Implement agile project management tools to facilitate collaboration and iteration between development teams
• Epic 2: "Create a Knowledge Base for Domain Expertise"
  • Description: Build a comprehensive knowledge base using open-source solutions like Nextra.
  • Enablers:
    • Integrate Nextra with the ticketing system and AI-powered task resolution system
    • Develop a scalable and reliable architecture to support large-scale knowledge base operations
• Epic 3: "Implement an Integrated Ticketing System"
  • Description: Develop a ticketing system that integrates with the knowledge base and AI-powered task resolution system.
  • Enablers:
    • Implement a robust and scalable architecture to support high-volume ticketing operations
    • Integrate ticketing system with existing ITSM tools and processes

Large Solution Level:

• Capability 1: "AI-Powered Task Resolution"
  • Description: Develop an intelligent task resolution system that leverages local LLMs.
  • Capabilities:
    • Develop a working prototype of the AI-powered task resolution system using local LLMs
    • Achieve an accuracy rate of at least 80% for task resolution suggestions generated by the system
  • Enablers:
    • Develop and deploy cloud infrastructure to support AI-powered task resolution suggestions
    • Implement agile project management tools to facilitate collaboration and iteration between development teams
• Capability 2: "Knowledge Base for Domain Expertise"
  • Description: Build a comprehensive knowledge base using open-source solutions like Nextra.
  • Capabilities:
    • Integrate Nextra with the ticketing system and AI-powered task resolution system
    • Develop a comprehensive knowledge base covering at least 5 domains, each with a minimum of 500 relevant articles and resources
  • Enablers:
    • Implement a scalable and reliable architecture to support large-scale knowledge base operations
    • Integrate knowledge base with existing ITSM tools and processes

Essential Level:

• Feature 1: "Develop AI-Powered Task Resolution Prototype"
  • Description: Develop a working prototype of the AI-powered task resolution system using local LLMs.
  • Stories:
    • Story 1: Develop a working prototype of the AI-powered task resolution system using local LLMs within 6 months
    • Story 2: Achieve an accuracy rate of at least 80% for task resolution suggestions generated by the system within the next 9 months
  • Enablers:
    • Develop and deploy cloud infrastructure to support AI-powered task resolution suggestions
    • Implement agile project management tools to facilitate collaboration and iteration between development teams
• Feature 2: "Integrate Nextra with Ticketing System"
  • Description: Integrate Nextra with the ticketing system and AI-powered task resolution system.
  • Stories:
    • Story 1: Integrate Nextra with the ticketing system within 3 months
    • Story 2: Achieve an accuracy rate of at least 80% for knowledge base searches within the next 9 months
  • Enablers:
    • Implement a scalable and reliable architecture to support large-scale knowledge base operations
    • Integrate knowledge base with existing ITSM tools and processes

Domain-Specific LLMs:

• Epic: "Integrate LLMs for Software Development"
  • Description: Integrate software development-specific LLMs with the AI-powered task resolution system.
  • Enablers:
    • Implement a robust and scalable architecture to support high-volume code suggestions operations
    • Integrate LLMs with existing software development tools and processes
• Capability: "LLMs for Software Development"
  • Description: Develop and integrate software development-specific LLMs with the AI-powered task resolution system.
  • Capabilities:
    • Integrate a software development-specific LLM with the AI-powered task resolution system within 6 months
    • Achieve an accuracy rate of at least 85% for code suggestions generated by the integrated LLM within the next 9 months
  • Enablers:
    • Implement agile project management tools to facilitate collaboration and iteration between development teams
    • Develop and deploy cloud infrastructure to support AI-powered task resolution suggestions

generate user stories

Here are some user stories for each of the features:

Feature 1: "Develop AI-Powered Task Resolution Prototype"

• Story 1: As a developer, I want to be able to input a task and receive relevant task resolution suggestions from a local LLM, so that I can complete tasks more efficiently.
  • Acceptance Criteria:
    • The system provides task resolution suggestions based on the input task
    • The suggestions are generated in real-time using a local LLM
    • The system allows users to filter and prioritize suggestions
• Story 2: As a developer, I want to be able to evaluate the accuracy of task resolution suggestions generated by the AI-powered task resolution system, so that I can improve the system's performance.
  • Acceptance Criteria:
    • The system provides metrics on the accuracy of task resolution suggestions
    • Users can view and analyze historical data on task resolution accuracy
    • The system recommends adjustments to input tasks or LLM parameters to improve accuracy

Feature 2: "Integrate Nextra with Ticketing System"

• Story 3: As a user, I want to be able to search for knowledge base articles related to my ticket, so that I can quickly find relevant information.
  • Acceptance Criteria:
    • The system integrates Nextra with the ticketing system
    • Users can search for knowledge base articles using relevant keywords and filters
    • The system provides a list of matching articles with summaries and ratings
• Story 4: As an administrator, I want to be able to manage and update the knowledge base, so that it remains accurate and relevant.
  • Acceptance Criteria:
    • The system allows administrators to add, edit, and delete knowledge base articles
    • Users can rate and comment on knowledge base articles
    • The system provides a dashboard for administrators to monitor article views and ratings

Feature 3: "Develop Knowledge Base"

• Story 5: As a user, I want to be able to access a comprehensive knowledge base with relevant information on various topics, so that I can quickly find answers to common questions.
  • Acceptance Criteria:
    • The system provides a comprehensive knowledge base with at least 5 domains and 500 articles
    • Users can search for articles using keywords and filters
    • The system provides a list of matching articles with summaries and ratings
• Story 6: As an administrator, I want to be able to update and manage the knowledge base, so that it remains accurate and relevant.
  • Acceptance Criteria:
    • The system allows administrators to add, edit, and delete knowledge base articles
    • Users can rate and comment on knowledge base articles
    • The system provides a dashboard for administrators to monitor article views and ratings

Feature 4: "Integrate LLMs for Software Development"

• Story 7: As a developer, I want to be able to use software development-specific LLMs to generate code suggestions, so that I can complete tasks more efficiently.
  • Acceptance Criteria:
    • The system integrates software development-specific LLMs with the AI-powered task resolution system
    • Users can input a task and receive relevant code suggestions from the LLM
    • The system allows users to filter and prioritize code suggestions
• Story 8: As a developer, I want to be able to evaluate the accuracy of code suggestions generated by the software development-specific LLM, so that I can improve the system's performance.
  • Acceptance Criteria:
    • The system provides metrics on the accuracy of code suggestions
    • Users can view and analyze historical data on code suggestion accuracy
    • The system recommends adjustments to input tasks or LLM parameters to improve accuracy

Make a Program Increment Plan based on 2-week sprints, and make a release plan.

Here is a Program Increment (PI) plan for the AI-Powered Task Resolution System:

Program Increment (PI):

• Duration: 8 weeks (4 sprints)
• Start Date: Week 1
• End Date: Week 8
• Objective:
  • Develop an initial prototype of the AI-powered task resolution system using local LLMs
  • Integrate Nextra with the ticketing system and AI-powered task resolution system
  • Develop a comprehensive knowledge base covering at least 5 domains, each with a minimum of 500 relevant articles and resources

Sprint 1: Week 1-2

• Objective: Develop an initial prototype of the AI-powered task resolution system using local LLMs
• Tasks:
  • Develop a working prototype of the AI-powered task resolution system using local LLMs (Epic 1)
  • Integrate software development-specific LLMs with the AI-powered task resolution system (Feature 4)
• User Stories:
  • Story 1: Develop a working prototype of the AI-powered task resolution system using local LLLMs
  • Story 7: Integrate software development-specific LLMs with the AI-powered task resolution system
• Deliverables:
  • A working prototype of the AI-powered task resolution system
  • Integration of software development-specific LLMs with the AI-powered task resolution system

Sprint 2: Week 3-4

• Objective: Integrate Nextra with the ticketing system and AI-powered task resolution system
• Tasks:
  • Integrate Nextra with the ticketing system (Feature 2)
  • Develop a comprehensive knowledge base covering at least 5 domains, each with a minimum of 500 relevant articles and resources (Epic 3)
• User Stories:
  • Story 3: Search for knowledge base articles related to my ticket
  • Story 5: Access a comprehensive knowledge base with relevant information on various topics
• Deliverables:
  • Integration of Nextra with the ticketing system
  • A comprehensive knowledge base covering at least 5 domains

Sprint 3: Week 5-6

• Objective: Develop and refine the AI-powered task resolution system
• Tasks:
  • Refine the AI-powered task resolution system using feedback from sprint 1 (Epic 1)
  • Develop a dashboard for administrators to monitor article views and ratings (Feature 3)
• User Stories:
  • Story 2: Evaluate the accuracy of task resolution suggestions generated by the AI-powered task resolution system
  • Story 6: Update and manage the knowledge base as an administrator
• Deliverables:
  • A refined AI-powered task resolution system
  • A dashboard for administrators to monitor article views and ratings

Sprint 4: Week 7-8

• Objective: Finalize and test the AI-Powered Task Resolution System
• Tasks:
  • Conduct final testing of the AI-powered task resolution system (Epic 1)
  • Develop a release plan for the AI-Powered Task Resolution System
• User Stories:
  • Story 8: Evaluate the accuracy of code suggestions generated by the software development-specific LLM
  • Story 9: Finalize and test the AI-powered task resolution system
• Deliverables:
  • A fully functional AI-powered task resolution system
  • A release plan for the AI-Powered Task Resolution System

Release Plan:

• Release Date: Week 8
• Release Candidate: The final version of the AI-Powered Task Resolution System developed during this PI
• Deployment Plan:
  1. Deploy the AI-powered task resolution system to a staging environment
  2. Conduct final testing and validation of the system
  3. Deploy the system to production

Note that this is just one possible Program Increment plan, and you may need to adjust it based on your specific project needs and requirements.