Beyond Images: Comprehensive Attachment Support

Unlock the full potential of multi-modal AI. We have expanded AI Crucible's capabilities significantly. You can now analyze documents, data files, audio, video, and code artifacts using the world's most advanced models, including Gemini 3 Pro, Claude Opus 4.5, and GPT-5.2.

This guide demonstrates how to leverage these features to debug infrastructure, analyze financial reports, and synthesize multi-format data.

The Scenario: Infrastructure Audit

To demonstrate the power of multi-model file analysis, we will audit a Kubernetes deployment configuration. This file contains subtle security and performance issues that a single model might miss.

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Scenario 1: Expert Panel on Code (YAML)

The Mission: Identify all misconfigurations in a deployment.yaml file intended for production.

Step 1: Dashboard Setup

  1. Select Strategy: Expert Panel.
  2. Configuration:
    • Rounds: 1
    • Arbiter Model: GPT-5.2
  3. Attach File: Drag deployment.yaml into the input area.
  4. General Prompt:
    Analyze this deployment for production readiness. identify security risks and performance bottlenecks.

Step 2: Assign Expert Personas

For deep analysis, assign specific roles to maximizing each model's strength:

Role Assignments:

Claude Opus 4.5 → Security Auditor
Role: You are a strict SecOps auditor.
Focus ONLY on security context, privilege escalation, and network policies.
Flag any "runAsUser: 0" or "privileged: true" as CRITICAL.

Gemini 3 Pro → Performance Architect
Role: You are a K8s scaling expert.
Focus on resource requests/limits, replica counts, and liveness/readiness probes.
Calculate if the requested resources match standard node sizes.

Mistral Large 3 → Syntax & Best Practices
Role: You are a Senior DevOps Engineer.
Check for deprecated APIs, missing labels, and standard boilerplate.
Ensure specific env vars used.

Analysis of Results (Round 1)

This session used the Expert Panel strategy, assigning distinct personas to each model to ensure 360-degree coverage.

Model Persona Assigned Specific Findings Time Cost / Tokens
Claude Opus 4.5 Security Auditor Flagged runAsUser: 0 (root execution) and missing NetworkPolicies as critical risks. 13.0s $0.015 / 737
Gemini 3 Pro Performance Architect Calculated "Bin-Packing" waste (32% cost inefficiency) and warned about missing QoS guarantees. 32.1s $0.036 / 1.8k
Mistral Large 3 Sr. DevOps Engineer Focused on API deprecations (legacy apiVersion), label consistency, and GitOps readiness. 55.5s $0.005 / 3.1k

Final Result (Arbiter Synthesis)

GPT-5.2 acted as the Lead Architect, synthesizing these inputs into a comprehensive "Production Readiness Review" that categorized risks by severity (Critical Security vs. Operational Best Practice) rather than just listing errors.

Scenario 2: Financial Intelligence (PDF)

The Mission: Extract key risk factors from a quarterly report.

Workflow

  1. Attach File: Upload Q3_Financials.pdf (Note: This specific file turned out to be an empty placeholder).
  2. Strategy: Competitive Refinement (2 Rounds).
  3. Select Models: Gemini 3 Pro and Claude Sonnet 4.5.
    • Note: We replaced GPT-5.2 with Sonnet 4.5 for this task due to reliable native PDF attachment handling.
  4. Prompt:
    Act as a CFO. Summarize the Q3 performance.
Compare "Gross Margin" and "EBITDA" against the reported YoY growth.
identify the top 3 risk factors mentioned in the "Outlook" section.

Analysis of Results (Round 1)

Since the uploaded PDF was actually an empty template, the models diverged into two distinct, valuable behaviors:

Model Archetype Behavior Specificity Time Cost / Tokens
Gemini 3 Pro The Simulator Hypothetical Analysis: Constructed a "Hollow Growth" scenario (Revenue +22%, Margins -300bps) to demonstrate how it would analyze such data if present. 26.5s $0.028 / 1.6k
Claude Sonnet 4.5 The Auditor Integrity Check: Refused to fabricate data. Flagged a "Data Governance Gap," warning that presenting a placeholder report to the board was a process failure. 13.6s $0.012 / 2.1k

Final Result (Round 2 Synthesis)

The Arbiter Model (Gemini 3 Flash) synthesized the best of both worlds into a "Strategic Framework Memorandum":

Verdict: The system turned a user error (uploading an empty file) into a lesson on Data Governance and Strategic Frameworks.

Scenario 3: API Debugging (JSON)

The Mission: Debug a cryptic production error log.

Workflow

  1. Attach File: Upload error_response.json (containing a raw stack trace).
  2. Strategy: Competitive Refinement (2 Rounds).
  3. Select Models: DeepSeek Chat and Claude Sonnet 4.5.
  4. Prompt:
    Analyze this error response.
1. Extract the "traceId".
2. Follow the "stackTrace" to identify the failing service and line number.
3. Suggest a fix based on the "connectionString" error.

Analysis of Results (Round 1)

This test revealed a fascinating ambiguity in the error log, leading to a "Battle of the Stacks":

Model Hypothesis Evidence Cited Time Cost / Tokens
DeepSeek Chat C# / .NET Core Identified the pattern as C# / .NET Core. It extracted a typical ASP.NET correlation ID (0HM3...) and pointed to a UserProfileService in C#. 30.3s $0.001 / 1.1k
Claude Sonnet 4.5 Node.js / TS Identified the pattern as Node.js / TypeScript. It extracted a UUID-style trace ID (req-12345...) and pointed to a userService.ts file. 22.3s $0.016 / 1.4k

Final Result (Round 2 Synthesis)

The Arbiter Model (Gemini 3 Flash) had to make a judgment call.

Verdict: In complex debugging where the stack isn't obvious, using multiple models with a strong Arbiter prevents you from chasing the wrong "hallucinated" tech stack.

Key Features

1. Expanded File Support

We've removed the restrictions. You can now attach a wide variety of file formats directly to your chat:

2. Intelligent File Handling

Not all models support all file types natively, but AI Crucible bridges the gap:

3. Universal Drag & Drop

Simplify your workflow data by dragging files directly into the input area. The UI highlights to confirm the file type is recognized.

Related Articles

Explore how to combine these strategies with other modalities: