#4582 Generic Framework for AI-Assisted Multi-Step, Deep-Dive Analyses

#4582 Generic Framework for AI-Assisted Multi-Step, Deep-Dive Analyses

#4582 Describe a generic framework for performing AI-assisted multi-step, deep-dive analyses on a given topic, for example: 1) mechanical systems breakdown into subsystems, components and parts, looking for opportunities to improve, 2) software algorithms improvement, 3) business operations improvement, 4) chemical compound analysis, looking for improvement and alternate solutions.

An effective AI-assisted multi-step deep-dive analysis relies on structured prompt-chaining and recursive decomposition. Because LLMs can lose context or summarize too aggressively when handled in a single prompt, a robust framework must break the topic down systematically, analyze the components, and then synthesize the findings.

Here is a generic, 4-step framework designed to handle any domain—whether it’s hardware, software, business, or chemistry.

The AI-Assisted Deep-Dive Framework

[ Phase 1: Decomposition ] ➔ [ Phase 2: Vector Search/Deepening ] ➔ [ Phase 3: Bottleneck/Opportunity ID ] ➔ [ Phase 4: Synthesis & Alternative Generation ]

Phase 1: Decomposition & Taxonomy (The "What")

The goal of this phase is to break a complex system into its absolute foundational elements so the AI doesn't overlook the "boring" details where inefficiencies often hide.

• AI Prompt Strategy: Ask the AI to act as a domain expert and map the target topic into a MECE (Mutually Exclusive, Collectively Exhaustive) hierarchy.

• Application Examples:

  • Mechanical: System ➔ Subsystems ➔ Components ➔ Individual Parts (e.g., Braking system ➔ Caliper assembly ➔ Piston, pads, seals).

  • Software: Monolith/Architecture ➔ Microservices ➔ Specific Algorithms ➔ Data structures & Sub-routines.

  • Business: Value Chain ➔ Core Processes ➔ Sub-processes ➔ Specific Tasks/KPIs.

  • Chemical: Target Compound ➔ Functional Groups/Molecular Scaffold ➔ Reaction Pathways ➔ Reagents/Catalysts.

Phase 2: Vector Search & Context Deepening (The "How It Works")

Once the system is broken down, you must feed the AI specific data, parameters, or industry benchmarks for the lowest-level components identified in Phase 1.

• AI Prompt Strategy: Use Retrieval-Augmented Generation (RAG) or web search to pull in current specifications, material properties, or industry standards for the components.

• What to feed the AI:

  • Mechanical: Stress/strain tolerances, material types, manufacturing costs.

  • Software: Big O time/space complexity, current CPU/memory utilization logs.

  • Business: Cycle times, labor costs, error rates, software friction points.

  • Chemical: Thermodynamic stability (Delta G), yield rates, toxicity profiles, cost of precursors.

Phase 3: Bottleneck & Inefficiency Identification (The "Why It's Failing/Suboptimal")

With the structure and data in place, instruct the AI to perform a critical evaluation. You are looking for the weakest links in the chain.

• AI Prompt Strategy: Instruct the AI to perform specific analytical frameworks (e.g., FMEA for mechanical, Profiling for software, Six Sigma/Lean for business, Retrosynthetic analysis for chemistry).

• Core Question for the AI: "Based on the data provided in Phase 2, which 20% of the components are causing 80% of the friction, cost, inefficiency, or failure?"

Domain	Typical Bottlenecks Looked For
Mechanical	High-wear parts, heavy materials, complex assembly steps, thermal stress.
Software	Nested loops, redundant DB queries, memory leaks, high network latency.
Business	Manual data entry, redundant approvals, communication silos, high churn steps.
Chemical	Expensive/rare catalysts, low-yield steps, hazardous waste byproducts.

Phase 4: Synthesis, Optimization & Alternative Generation (The "What Next")

The final phase moves from analysis to creation. The AI is tasked with generating alternative configurations, materials, or methods to solve the bottlenecks identified in Phase 3.

• AI Prompt Strategy: Force the AI out of standard thinking by applying specific constraints (e.g., "Reduce cost by 30%," "Switch to a green chemistry alternative," "Convert from O(N^2) to O(N \log N)").

• Output Deliverable: A structured matrix comparing the baseline system against 2–3 AI-generated alternative solutions, ranked by feasibility, cost, and impact.

Best Practices for Running this Framework

💡 Keep a Running "Context Document": Do not do this all in one giant prompt. Run Phase 1, review and edit the output, and paste the approved taxonomy into Phase 2. This prevents "AI hallucination drift."

🤖 Role-Prompting: Change the AI's persona at each step. In Phase 1 and 2, it is a Pragmatic Systems Auditor. In Phase 4, switch its persona to a Creative/Disruptive R&D Engineer.

The application should support Ollama-supported LLMs, all results should be written to a database, follow-up SQL queries developed, and summary of the study written as markdown and saved to file in the designated work space.

To implement this framework as an automated, programmatic application supporting Ollama-hosted LLMs, you can construct a pipeline using Python. This application will handle orchestration, write intermediate and final analytical data to a relational database, execute follow-up SQL queries for metric extraction, and export the final report to a Markdown file.

Here is a technical blueprint and implementation guide for building this application.

Architecture & Tech Stack

• Orchestration: Python (requests or langchain_community.llms.Ollama)

• LLM Engine: Ollama (running models like llama3, mistral, or phi3 locally)

• Storage: SQLite or PostgreSQL (for tracking phases and metrics)

• File Export: Standard Python OS/IO operations

Step 1: Database Schema Design

We need a schema to track the execution of the deep dive across different phases, allowing us to query bottlenecks and alternative solutions later.

-- Track the overall analysis project
CREATE TABLE IF NOT EXISTS analysis_projects (
    project_id TEXT PRIMARY KEY,
    topic TEXT NOT NULL,
    domain TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Store data generated at each phase
CREATE TABLE IF NOT EXISTS phase_results (
    result_id INTEGER PRIMARY KEY AUTOINCREMENT,
    project_id TEXT,
    phase_number INTEGER,
    phase_name TEXT,
    raw_output TEXT,
    structured_metrics JSON, -- Store cost, efficiency, or complexity scores
    FOREIGN KEY(project_id) REFERENCES analysis_projects(project_id)
);

Step 2: The Core Python Application

The following script initializes a project, loops through the framework phases using an Ollama LLM, logs results to the database, and generates the final markdown report.

import os
import json
import sqlite3
import requests

# Configuration
OLLAMA_URL = "http://localhost:11434/api/generate"
MODEL_NAME = "llama3"  # Change to any model pulled via 'ollama pull'
DB_PATH = "deep_dive_analysis.db"
WORKSPACE_DIR = "./workspace"

os.makedirs(WORKSPACE_DIR, exist_ok=True)

def query_ollama(prompt, context_history=""):
    """Helper function to stream/query Ollama API."""
    full_prompt = f"{context_history}\n\nTask: {prompt}" if context_history else prompt
    payload = {
        "model": MODEL_NAME,
        "prompt": full_prompt,
        "stream": False,
        "options": {"temperature": 0.2} # Low temp for analytical accuracy
    }
    try:
        response = requests.post(OLLAMA_URL, json=payload)
        return response.json().get("response", "")
    except Exception as e:
        return f"Error connecting to Ollama: {str(e)}"

def init_db():
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("""
    CREATE TABLE IF NOT EXISTS analysis_projects (
        project_id TEXT PRIMARY KEY, topic TEXT, domain TEXT, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    );""")
    cursor.execute("""
    CREATE TABLE IF NOT EXISTS phase_results (
        result_id INTEGER PRIMARY KEY AUTOINCREMENT, project_id TEXT,
        phase_number INTEGER, phase_name TEXT, raw_output TEXT,
        FOREIGN KEY(project_id) REFERENCES analysis_projects(project_id)
    );""")
    conn.commit()
    return conn

def run_deep_dive(project_id, topic, domain, steps):
    conn = init_db()
    cursor = conn.cursor()
    
    # Save Project
    cursor.execute("INSERT OR IGNORE INTO analysis_projects (project_id, topic, domain) VALUES (?, ?, ?)", 
                   (project_id, topic, domain))
    conn.commit()
    
    running_context = f"We are performing a deep-dive analysis on the following topic: {topic} within the domain: {domain}.\n"
    
    for phase_num, (phase_name, prompt) in enumerate(steps, start=1):
        print(f"Executing Phase {phase_num}: {phase_name}...")
        
        # Get response from local LLM
        output = query_ollama(prompt, context_history=running_context)
        
        # Save results to DB
        cursor.execute("""
            INSERT INTO phase_results (project_id, phase_number, phase_name, raw_output)
            VALUES (?, ?, ?, ?)
        """, (project_id, phase_num, phase_name, output))
        conn.commit()
        
        # Append to running context so subsequent steps build on top of previous data
        running_context += f"\n### Results from Phase {phase_num} ({phase_name}):\n{output}\n"
        
    conn.close()
    print("Analysis execution complete. Results saved to database.")

# Define the Framework Steps
prompts_pipeline = [
    ("Decomposition", "Break this system down into its core components, sub-processes, or structures using a MECE taxonomy."),
    ("Context Deepening", "For the components identified, outline typical industry baseline metrics, constraints, or common operating parameters."),
    ("Bottleneck Identification", "Analyze the system and identify the primary bottlenecks, friction points, or systemic inefficiencies. Highlight the top 20% causing 80% of issues."),
    ("Optimization Alternatives", "Provide 2 structural or architectural alternatives to mitigate the identified bottlenecks. Compare them based on ease of implementation and expected impact.")
]

# Run the system for a specific topic
project_uuid = "proj_software_001"
run_deep_dive(
    project_id=project_uuid, 
    topic="Optimizing a Python-based REST API handling high write-volumes to PostgreSQL", 
    domain="Software Engineering", 
    steps=prompts_pipeline
)

Step 3: Follow-Up SQL Queries (Data Post-Processing)

Once data is in the database, you can run diagnostic SQL queries to aggregate, audit, or extract specific findings from the text outputs (or structured JSON data if extracted).

Query 1: Extract Bottlenecks and Alternatives for a Specific Project

This extracts just the crucial creative/problem-solving components of the study.

SELECT phase_name, raw_output 
FROM phase_results 
WHERE project_id = 'proj_software_001' 
  AND phase_number IN (3, 4)
ORDER BY phase_number ASC;

Query 2: System Audit / Length Verification

Ensures that the LLM didn't truncate or provide sparse outputs for detailed phases.

SELECT phase_name, LENGTH(raw_output) as response_character_count
FROM phase_results
WHERE project_id = 'proj_software_001'
ORDER BY phase_number;

Step 4: Markdown Generation and Workspace Export

To compile the study from the database into a cleanly formatted .md file inside your designated workspace, execute this processing function:

def export_summary_to_workspace(project_id):
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    
    # Fetch metadata
    cursor.execute("SELECT topic, domain, created_at FROM analysis_projects WHERE project_id = ?", (project_id,))
    project = cursor.fetchone()
    if not project:
        print("Project not found.")
        return
    
    topic, domain, date = project
    
    # Fetch phases
    cursor.execute("SELECT phase_number, phase_name, raw_output FROM phase_results WHERE project_id = ? ORDER BY phase_number ASC", (project_id,))
    phases = cursor.fetchall()
    
    # Construct Markdown content
    markdown_content = f"# Deep-Dive Analysis Report: {topic}\n"
    markdown_content += f"**Domain:** {domain} | **Project ID:** {project_id} | **Date:** {date}\n\n"
    markdown_content += "---\n\n## Executive Summary\nThis report compiles the multi-step AI-assisted deep-dive analysis performed sequentially.\n\n"
    
    for num, name, output in phases:
        markdown_content += f"## Phase {num}: {name}\n"
        markdown_content += f"{output}\n\n"
        markdown_content += "---\n\n"
        
    markdown_content += "*End of Generated Report.*"
    
    # Write to File
    file_path = os.path.join(WORKSPACE_DIR, f"{project_id}_summary.md")
    with open(file_path, "w", encoding="utf-8") as file:
        file.write(markdown_content)
        
    conn.close()
    print(f"Report successfully compiled and saved to: {file_path}")

# Run Export
export_summary_to_workspace("proj_software_001")

Pydantic must be used for structured outputs that can be further broken down in following steps

To build a chainable, multi-step analysis framework using Ollama and Pydantic, we must leverage Ollama's native structured outputs (format=Model.model_json_schema()). This allows you to enforce schema compliance so that the data from Phase 1 can be programmatically parsed, stored, and extracted to feed directly into subsequent phases.

Here is the updated, production-grade codebase utilizing Pydantic for structured chaining, SQLite for persistent storage, and automatic markdown reporting.

1. Pydantic Schemas for Chaining Phases

We define dedicated Pydantic models for each step of the pipeline. Notice how the output of Phase 1 (SystemDecomposition) provides the exact structural keys needed to loop through and target bottlenecks in Phase 2 and 3.

from pydantic import BaseModel, Field
from typing import List, Optional

# --- Phase 1: Decomposition Schema ---
class ComponentPart(BaseModel):
    part_name: str = Field(..., description="Name of the specific sub-component or part.")
    function: str = Field(..., description="Core function or purpose within the system.")

class Subsystem(BaseModel):
    subsystem_name: str = Field(..., description="Name of the subsystem group.")
    components: List[ComponentPart] = Field(..., description="Components making up this subsystem.")

class SystemDecomposition(BaseModel):
    topic: str
    subsystems: List[Subsystem]

# --- Phase 2: Context & Bottlenecks Schema ---
class BottleneckAnalysis(BaseModel):
    target_component: str = Field(..., description="The part or component causing issues.")
    current_metrics: str = Field(..., description="Estimated or benchmark operating metrics (e.g., latency, weight, cost).")
    failure_mode_or_friction: str = Field(..., description="Why this component is a bottleneck (the 80/20 rule).")

class SystemBottlenecks(BaseModel):
    bottlenecks: List[BottleneckAnalysis]

# --- Phase 3: Optimization & Alternatives Schema ---
class AlternativeSolution(BaseModel):
    alternative_name: str
    description: str = Field(..., description="Detailed description of the new approach or material.")
    expected_impact: str = Field(..., description="Quantifiable improvement (e.g., '30% reduction in database lock time').")
    feasibility_score: int = Field(..., ge=1, le=5, description="Feasibility from 1 (hardest) to 5 (easiest).")

class OptimizationReport(BaseModel):
    proposed_alternatives: List[AlternativeSolution]

2. Updated Python Application Orchestrator

This script executes the pipeline, enforces schemas via Ollama, maps the objects directly to JSON strings, and writes them straight to the SQLite DB.

import os
import json
import sqlite3
import ollama  # Uses the official ollama python library

DB_PATH = "structured_deep_dive.db"
WORKSPACE_DIR = "./workspace"
MODEL_NAME = "llama3.1" # Standard model supporting structured JSON schemas

os.makedirs(WORKSPACE_DIR, exist_ok=True)

def init_db():
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("""
    CREATE TABLE IF NOT EXISTS analysis_projects (
        project_id TEXT PRIMARY KEY, topic TEXT, domain TEXT, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    );""")
    cursor.execute("""
    CREATE TABLE IF NOT EXISTS phase_results (
        result_id INTEGER PRIMARY KEY AUTOINCREMENT, project_id TEXT,
        phase_number INTEGER, phase_name TEXT, structured_json TEXT,
        FOREIGN KEY(project_id) REFERENCES analysis_projects(project_id)
    );""")
    conn.commit()
    return conn

def execute_structured_deep_dive(project_id: str, topic: str, domain: str):
    conn = init_db()
    cursor = conn.cursor()
    
    cursor.execute("INSERT OR IGNORE INTO analysis_projects (project_id, topic, domain) VALUES (?, ?, ?)", 
                   (project_id, topic, domain))
    conn.commit()

    # ==========================================
    # PHASE 1: Decomposition
    # ==========================================
    print("Executing Phase 1: Decomposition...")
    prompt_p1 = f"Deconstruct this system into an exhaustive taxonomy of subsystems and components: {topic}"
    
    res_p1 = ollama.chat(
        model=MODEL_NAME,
        messages=[{"role": "user", "content": prompt_p1}],
        format=SystemDecomposition.model_json_schema(),
        options={"temperature": 0.0}
    )
    
    # Validate and save
    p1_data = SystemDecomposition.model_validate_json(res_p1.message.content)
    cursor.execute("INSERT INTO phase_results (project_id, phase_number, phase_name, structured_json) VALUES (?, 1, 'Decomposition', ?)",
                   (project_id, p1_data.model_dump_json()))
    
    # ==========================================
    # PHASE 2: Bottleneck Identification (Chained using data from Phase 1)
    # ==========================================
    print("Executing Phase 2: Bottleneck Identification...")
    prompt_p2 = f"""Analyze the following broken down system components and locate the top 20% inefficiencies or critical engineering/operational bottlenecks.
    System Layout: {p1_data.model_dump_json()}"""
    
    res_p2 = ollama.chat(
        model=MODEL_NAME,
        messages=[{"role": "user", "content": prompt_p2}],
        format=SystemBottlenecks.model_json_schema(),
        options={"temperature": 0.1}
    )
    
    p2_data = SystemBottlenecks.model_validate_json(res_p2.message.content)
    cursor.execute("INSERT INTO phase_results (project_id, phase_number, phase_name, structured_json) VALUES (?, 2, 'Bottlenecks', ?)",
                   (project_id, p2_data.model_dump_json()))

    # ==========================================
    # PHASE 3: Optimization & Alternatives (Chained using data from Phase 2)
    # ==========================================
    print("Executing Phase 3: Alternative Solutions...")
    prompt_p3 = f"""Provide optimized architectural or material alternatives to fix these specific bottlenecks:
    Bottlenecks identified: {p2_data.model_dump_json()}"""
    
    res_p3 = ollama.chat(
        model=MODEL_NAME,
        messages=[{"role": "user", "content": prompt_p3}],
        format=OptimizationReport.model_json_schema(),
        options={"temperature": 0.2}
    )
    
    p3_data = OptimizationReport.model_validate_json(res_p3.message.content)
    cursor.execute("INSERT INTO phase_results (project_id, phase_number, phase_name, structured_json) VALUES (?, 3, 'Optimization', ?)",
                   (project_id, p3_data.model_dump_json()))
    
    conn.commit()
    conn.close()
    print("Pipeline processed successfully and recorded in DB.")

# Run the pipeline
project_uuid = "proj_mech_002"
execute_structured_deep_dive(
    project_id=project_uuid,
    topic="High-performance hydraulic braking assembly for electric vehicles",
    domain="Mechanical Systems"
)

3. Follow-Up SQL Queries

Because the outputs are stored cleanly as deterministic JSON strings inside the SQLite DB, you can easily run analytical SQL queries leveraging SQLite's built-in JSON extraction functions (json_each or json_extract).

Query 1: Extract and Flatten All Key Bottlenecks across Projects

SELECT 
    project_id,
    json_extract(value, '$.target_component') AS component,
    json_extract(value, '$.failure_mode_or_friction') AS friction_reason
FROM phase_results, 
json_each(phase_results.structured_json, '$.bottlenecks')
WHERE phase_name = 'Bottlenecks';

Query 2: Filter Only High-Feasibility Strategic Solutions

Extract solutions from Phase 3 where the AI scored feasibility $\ge 4$:

SELECT 
    project_id,
    json_extract(value, '$.alternative_name') AS solution,
    json_extract(value, '$.expected_impact') AS impact,
    json_extract(value, '$.feasibility_score') AS feasibility
FROM phase_results, 
json_each(phase_results.structured_json, '$.proposed_alternatives')
WHERE phase_name = 'Optimization' 
  AND CAST(json_extract(value, '$.feasibility_score') AS INT) >= 4;

4. Markdown Report Generator

This script pulls the structured data objects from the DB, iterates through them, and outputs a highly polished report safely generated as a file inside the designated workspace.

def generate_markdown_report(project_id: str):
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    
    cursor.execute("SELECT topic, domain, created_at FROM analysis_projects WHERE project_id = ?", (project_id,))
    project = cursor.fetchone()
    if not project: return
    topic, domain, date = project
    
    md = f"# Deep-Dive Engineering Report: {topic}\n"
    md += f"**Domain:** {domain} | **Project ID:** {project_id} | **Date Analyzed:** {date}\n\n---\n\n"
    
    # Format Phase 1
    cursor.execute("SELECT structured_json FROM phase_results WHERE project_id = ? AND phase_number = 1", (project_id,))
    p1 = json.loads(cursor.fetchone()[0])
    md += "## 1. System Taxonomy Decomposition\n"
    for sub in p1['subsystems']:
        md += f"### Subsystem: {sub['subsystem_name']}\n"
        for comp in sub['components']:
            md += f"- **{comp['part_name']}**: {comp['function']}\n"
        md += "\n"
        
    # Format Phase 2
    cursor.execute("SELECT structured_json FROM phase_results WHERE project_id = ? AND phase_number = 2", (project_id,))
    p2 = json.loads(cursor.fetchone()[0])
    md += "---\n\n## 2. Identified Inefficiencies & Bottlenecks\n"
    md += "| Target Component | Baseline / Benchmark Metrics | Failure Mode / Friction |\n| :--- | :--- | :--- |\n"
    for b in p2['bottlenecks']:
        md += f"| **{b['target_component']}** | {b['current_metrics']} | {b['failure_mode_or_friction']} |\n"
    md += "\n"

    # Format Phase 3
    cursor.execute("SELECT structured_json FROM phase_results WHERE project_id = ? AND phase_number = 3", (project_id,))
    p3 = json.loads(cursor.fetchone()[0])
    md += "---\n\n## 3. Recommended Optimization Alternatives\n"
    for alt in p3['proposed_alternatives']:
        md += f"### 💡 Alternative: {alt['alternative_name']} (Feasibility: {alt['feasibility_score']}/5)\n"
        md += f"- **Description:** {alt['description']}\n"
        md += f"- **Expected Impact:** {alt['expected_impact']}\n\n"
        
    file_path = os.path.join(WORKSPACE_DIR, f"{project_id}_deep_dive.md")
    with open(file_path, "w", encoding="utf-8") as f:
        f.write(md)
        
    conn.close()
    print(f"Markdown compiled and generated successfully at: {file_path}")

# Run compilation
generate_markdown_report("proj_mech_002")

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