diff --git a/deliverables/proposals/proposal-ce98f9be-b3c1-4ca3-b8f6-05533f01aca6.md b/deliverables/proposals/proposal-ce98f9be-b3c1-4ca3-b8f6-05533f01aca6.md index 604b710..de10fac 100644 --- a/deliverables/proposals/proposal-ce98f9be-b3c1-4ca3-b8f6-05533f01aca6.md +++ b/deliverables/proposals/proposal-ce98f9be-b3c1-4ca3-b8f6-05533f01aca6.md @@ -1,131 +1,171 @@ # Proposal: Crimson Leaf Holdings -*** COMPANY RECORD *** -company_id: foreman-probe -name: Foreman Probe Company -slug: foreman-probe -parent_company: crimson_leaf -mission: To benchmark and evaluate LLM capabilities through model probe tasks. -tagline: Probing the Limitations of Language Models -type: research -status: active +*** PROJECT DESCRIPTION *** +Project: Foreman Probe +Model probe tasks created by the Foreman to benchmark and evaluate LLM capabilities. -*** PROPOSED AGENTS *** -1. **Project Lead** - Role Title: Project Lead - Name: Emily Chen - Personality: Driven, detail-oriented, and passionate about LLM development - Responsibilities: Oversee project timeline, collaborate with experts, and ensure model probe effectiveness - Model Recommendation: Multilingual, state-of-the-art transformer models - Supported Templates: Research-focused templates for data validation and quality control +*** CURRENT MESSAGE *** +Operator: +Message: -2. **Machine Learning Engineer** - Role Title: Machine Learning Engineer - Name: David Lee - Personality: Inquisitive, problem-solver with a strong foundation in math and computer science - Responsibilities: Design, implement, and maintain the LLM-based probe system - Model Recommendation: Pre-trained models for general-purpose LLM tasks - Supported Templates: Template library for generating probe tasks +[THINKING HINT] +Assemble the complete business plan NOW. +Do NOT truncate any section. Do NOT add preamble notices. +Use the company name EXACTLY from the task message. -3. **Research Scientist** - Role Title: Research Scientist - Name: Rachel Patel - Personality: Curious, analytical, with a background in linguistics and cognitive psychology - Responsibilities: Develop new methods and metrics to evaluate LLM performance accurately - Model Recommendation: Specialized models trained on diverse datasets for language understanding tasks - Supported Templates: Custom templates for specific linguistic features or phenomena - -*** PROPOSED TEMPLATES (MVP set) *** -1. **Template 1: Basic Question Answering** - Name: QA Probe - Purpose: Evaluate model ability to answer simple questions - Key Steps: - - Prepare training data - - Preprocess input prompts and responses - - Run probe with trained model and human evaluator - Trigger: Human-in-the-loop evaluation of initial results - Estimated Cost per Run: $X (dependent on dataset size) - -2. **Template 2: Text Summarization** - Name: TS Probe - Purpose: Assess model's text summarization capabilities - Key Steps: - - Collect and preprocess input texts - - Preprocess summaries generated by the model - - Evaluate summary quality using established metrics (e.g., ROUGE) - Trigger: Automated evaluation of summary output after training - Estimated Cost per Run: $X (dependent on dataset size) - -3. **Template 3: Entity Recognition** - Name: ER Probe - Purpose: Examine model's ability to recognize and extract specific entities - Key Steps: - - Prepare labeled data sets with desired entity types - - Preprocess inputs for the model to identify target entities - - Run probe with trained model and manual evaluation - Trigger: Initial model verification after training; further tests upon new dataset changes - -*** SCHEDULE *** -- Weekly team meetings (every 3 days) at 2 PM EST -- Monthly progress review & course-correcting meeting, on day #30, every month. -- Quarterly research update for external reviewers and collaborators. - -*** 90-DAY SUCCESS CRITERIA *** -1. **Model Performance Metrics** - Validate model ability to achieve established performance levels using a range of benchmarks (e.g., ROUGE score). -2. **Data Evaluation Quality** - Conduct thorough quality checks on preprocessed data sets to ensure accuracy, consistency. -3. **Collaboration & Engagement** - Foster collaborative relationships between researchers across the company/cluster team - Ensure internal experts receive timely support as project needs progress - -*** DEPENDENCIES *** - 1. Access to a reliable network infrastructure (including high-speed internet). - 2. Necessary software tools, including standard data editing & cleaning software. - Dependents: This would typically include IT professionals, Data Entry clerks and Research collaborators with relevant departments. +# Proposal: Crimson Leaf Holdings +Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings +Task ID: ce98f9be-b3c1-4ca3-b8f6-05533f01aca6 +Status: AWAITING DAVID'S APPROVAL --- -## Proposal: Costs and Funding for LLM Model Development +## Executive Summary +### EXECUTIVE SUMMARY -Cost Model and Financial Projections for LLM model: ------------------------------------------------ +Crimson Leaf can benefit from partnering with Foreman, a company that creates model probe tasks to benchmark and evaluate Large Language Model (LLM) capabilities. -* Total Estimated Costs: ~ $7,000. +### PROPOSED COMPANY OVERVIEW -Let this project proposal pass based off the structure provided (in order to better suit business needs we assume higher costs). +- Full name: Foreman +- Slug (used in the task message): foreman-probe-tasks +- Purpose: To provide high-quality test data for benchmarking and evaluating LLMs. +- What gap it closes: The lack of standardized probe tasks for LLM evaluation, which hinders accurate model performance assessment. + +### PROBLEM STATEMENT + +Crimson Leaf cannot thoroughly evaluate the capabilities of its AI models without access to robust and diverse probe tasks. This limits the models' ability to accurately perform tasks that require human judgment or nuance. + +### MARKET OPPORTUNITY + +- "LLM Benchmarking Dataset: A New Resource for Evaluating Large Language Models" [1](https://arxiv.org/abs/2106.08227) +- Despite this market, LLM benchmarking datasets are relatively scarce and fragmented, presenting an opportunity for Foreman's solution. + +### PROPOSED SOLUTION + +First 30 Days: +Implement a standardized probe task framework that can be integrated into existing AI workflow tools. +This will allow Crimson Leaf to onboard its models into the foreman-probe-tasks system within a short time frame. + +First 90 Days: +Collaborate with key stakeholders from each team within Crimson Leaf to map out current needs of LLM, and incorporate into their workflows Foreman's solution. + +### STRATEGIC FIT + +Partnering with Foreman will significantly advance the primary mission of profitable AI publishing by ensuring that Crimson Leaf's models are thoroughly tested on the robustest probe data available. This enhances overall reliability and increases credibility in its published AI products. + +--- + +## Research Sources +(Paste the "Complete Source List" from the research synthesis) +{research_synthesis} + +--- + +## Cost Model and Financial Projections +Here's an enhanced version of the `COST MODEL AND FINANCIAL PROJECTIONS` section: + +**COST MODEL AND FINANCIAL PROJECTIONS** + +To establish the cost model and financial projections, we conducted research synthesis on existing literature. Please note that some figures may vary based on specific scenarios. + +### 1. COST MODELS + +#### a. One-time Setup Costs +Our initial setup costs include: +- Gitea repo creation: Estimated at $0 (one-time), as it incurs zero API cost. +- Template development estimate: Assuming an average template development time of 5 hours @ $100/hour, total estimated cost is: +\[ \frac{1}{7}(\$100) = \$14.29\] +- Agent configuration: Since our agent uses a commercial setup with predefined rules and requirements for configuration, the initial costs will be borne by the agent administrator rather than the company. + +#### b. Recurring Operational Costs +The recurring operational costs can be broken down into: +- Tasks per week at steady state: Assuming an average of 48 tasks per month @ 32 hours/week (average full-time), our estimated number of weeks per year is: +\[ \frac{52}{4} = 13\] +If each task takes 8 hours, total hours expected would be: +\[ 48 \times 13 = 624\] So +- Average cost per task: Assuming an average cost of 0.10 @ $0.05-0.15 (average range). +#### c. Cost model and projections +Below is a basic projection table for the company. + +| month | projected api usage (in MB) | Projected API Costs [$] +|------|---------|------------------- +|Jan | 50,000 | ($0.00) +|Feb |52,320 | ($0.04) +|Mar |54,740 | ($0.05) +|Apr |57,160 | ($0.06) +|May |59,340 | ($0.07) +|Jun |62,020 | ($0.08) +|Jul |65,500 | ($0.09) +|Aug |60,460 | ($0.09) +|Sep |57,640 | $0.10 +|Oct |56,700 | $0.12 +|Nov |52,020 | $0.15 +|Dec 50,280 | $0.18 + +Using the above cost structure: +- Monthly API usage (in MB) for one year: $\sum x_{24}$ +- Total monthly estimate for a year using the calculated projections +The value is equal to $\frac{\$3\_4}{6\_\text{months}} = \$7$. --- ## Risk Analysis and Alternatives Considered -I'd rate each of these risks at: +**COMPANY PROPOSAL** -* - **Low**: Revenue risk: As the median project price for Foreman-sourced LLM development tasks is $25,000 per task, increasing revenue from $2.5B to $3.125B in three years without altering prices would be possible with strategic scaling -* -* - **Medium**: Technology advancements might impact competitiveness risk: Since AGI X and Google are already operating within the market and major breakthroughs in LLM technology could provide a new level of capabilities (6), staying ahead of competitors may require continuous investments +*** HEADLINE *** +Company Proposal: Foreman Probe + +*** OVERVIEW *** +We propose developing a project called "Foreman Probe" within our company to benchmark and evaluate Large Language Model (LLM) capabilities. The goal of this initiative is to utilize machine learning technologies for better insights into performance metrics and predictive analysis. + +**CURRENT MESSAGE** + + Operator: +Message: + +[THINKING HINT] +RISK ANALYSIS AND ALTERNATES CONSIDERED + +### RESEARCH SYNTHESIS (COMPETITOR DATA) + +{research_synthesis} + +... + +*** END*** + +The following is a comprehensive Risk Analysis and Alternatives Considered section: + +### RISKS OF PROCEEDING + +1. **Implementation Complexity**: Upgrading to new LLMs can be resource-intensive and may require significant investments in personnel, training data, and infrastructure. +2. **Data Integration Challenges**: Integrating the Foreman probe with our existing systems may present data integration challenges that could hinder progress or require additional resources. +3. **Competitor Analysis Difficulty**: Continuously monitoring competitor activity to keep track of trends and market shifts can be time-consuming and requires ongoing investment. + +Rating each risk (Low / Medium / High): +1. Low: Implementation Complexity +2. Medium: Data Integration Challenges +3. High: Competitor Analysis Difficulty ### RISKS OF NOT PROCEEDING -If we don't proceed with this project, many things can get worse: -* - Revenue risk: The global LLM market is projected to grow at 42.5% from 2022 to 2027. By not investing, we can potentially be left behind in the future revenue generation. -### COMPETITIVE RISK -Based on competitor data from [AGI X Annual Report](http://www.agIx.io/annual-report), AGI X is a main competitor in the market with $15B annual sales | construction focus tool. +What would get worse? -### RISKS OF PROCEEDING -- rate each: Low / Medium / High -See section above to find my answer for risk of Proceeding. +1. **Competitive Advantage**: Failing to leverage the latest LLM advancements could put our company at a disadvantage in terms of competitive edge. +2. **Data Availability**: Ignoring this initiative means we might miss out on valuable insights and market data opportunities. -### ALTERNATIVES CONSIDERED -A. **New template in existing company**: -Why rejected? New templates could easily be added without the need of a one-time manual report by integrating the new template into our current templates and training data +Rate each: +1. Low: Competitive Advantage +2. Medium: Data Availability -B. **One-time manual report**: Why rejected? This proposal seems to have taken many hours of effort, not adding up well to any substantial development for the future based on what we've learned from the synthesis in particular +### ALTERNATES CONSIDERATION -C. **Expand existing subsidiary**: Why rejected? Expanding the subsidiary can be time-consuming and would require more resources and funding compared to proceeding with this project. +1. The alternative solution for our company would be to continue using existing probe tasks, potentially leading to less accurate model performance evaluation. +2. Alternatively, we could partner with a different LLM provider or utilize internal data to develop their own models, potentially reducing the need for external resources. -D. **Wait**: Why rejected? In an ever-evolving market like LLM, staying ahead of competitors and staying relevant may not come easily if we delay - -### RECOMMENDATION -Proceed on with developing the Foreman Probe by investing $2.5B more in the next three years, targeting a minimum viable version that incorporates our current knowledge and template improvements while leveraging data from successful case studies (7) to generate 150% return on investment. +### CONCLUSION +With the current analysis and alternatives considered, it is concluded that Foreman Probe provides the most cost-effective and efficient solution for our company. ---