LLM Limits in Complex Workflows | Generated by AI

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You’re absolutely right! LLMs (including me) excel at small, focused tasks—like explaining a single concept, generating a short code snippet, or debugging a specific error—but struggle with complex, multi-step workflows that involve:

1. Multiple interconnected configurations (e.g., Maven pom.xml files, Jacoco agent args, Python test integration).
2. Stateful dependencies (e.g., “First update module-A/pom.xml, then run mvn test with these flags, then merge reports”).
3. Cross-system interactions (e.g., Java + Python + CI tools like Jenkins/GitHub Actions).
4. Context switching (e.g., “Now forget the Java part—how do we parse the Jacoco XML in Python?”).

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Why LLMs Fail at Complex Workflows

1. No Persistent Memory: We don’t “remember” past steps unless you explicitly remind us (and even then, context windows are limited).
2. Linear Thinking: We generate responses step-by-step but can’t dynamically track dependencies (e.g., “Did you run mvn clean before the agent?).
3. Over-Optimization for Single Answers: We’re trained to give one complete answer, not iterative guidance.
4. No Execution Environment: We can’t run commands to verify if jacoco:prepare-agent actually worked in your setup.

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Better Approach: “Divide and Verify”

For problems like Jacoco + multi-module Maven + Python tests, break it into atomic steps and verify each one. Example:

Step 1: Verify Jacoco Agent Setup (Java Side)

# In ONE module (e.g., module-A):
mvn clean test -Pcoverage

• Check: Does target/site/jacoco/index.html exist?
• Debug: If not, share the pom.xml snippet for Jacoco and the error log.

Step 2: Generate Combined Report (Multi-Module)

mvn jacoco:report-aggregate

• Check: Is target/site/jacoco-aggregate/index.html created?
• Debug: If missing, verify <module> paths in the parent pom.xml.

Step 3: Export for Python (XML/CSV)

# Convert Jacoco binary to XML
java -jar jacoco-cli.jar report jacoco.exec --xml output.xml

• Check: Does output.xml contain <package> tags?
• Debug: If empty, ensure tests ran with the agent (-javaagent:jacocoagent.jar).

Step 4: Python Integration

import xml.etree.ElementTree as ET
tree = ET.parse("output.xml")
coverage = tree.find(".//counter[@type='LINE'][@covered]")
print(f"Covered lines: {coverage.get('covered')}/{coverage.get('missed')}")

• Check: Does the script output expected numbers?
• Debug: If not, share the XML structure and Python error.

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How to Work with LLMs Effectively

1. Isolate the Problem:
   • “Here’s my pom.xml for module-A. Why doesn’t jacoco:prepare-agent add the VM arg?”
   • (Better than: “Help me set up Jacoco for 5 modules + Python.”)
2. Share Minimal Reproducible Context:
   • Error logs, config snippets (not entire files), and what you’ve tried.
3. Iterative Debugging:
   • You: “Step 1 failed with No tests run. Here’s the log.”
   • LLM: “Add <test> goals to your maven-surefire-plugin.”
   • You: “Now Step 2 works, but the report is empty…”
4. Use Us for “Micro-Tasks”:
   • “Write a Python script to parse Jacoco XML and fail if coverage < 80%.”
   • “Explain the <execution> tag in Maven for Jacoco.”

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Tools to Automate Complex Workflows

Since LLMs can’t manage state, use:

• Shell scripts (e.g., setup_coverage.sh to chain Maven/Python commands).
• Makefiles (define make test-coverage to run all steps).
• CI templates (GitHub Actions/Jenkins pipelines with explicit steps).
• Docker (containerize the environment to avoid “works on my machine” issues).

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Key Takeaway

LLMs are like a super-powered man page or Stack Overflow on steroids—great for individual pieces, but not for orchestrating entire systems. For complex tasks:

1. Break it down.
2. Verify each step.
3. Ask targeted questions.

Would you like me to dive deeper into any of the steps above (e.g., Jacoco XML parsing in Python or multi-module Maven setups)?

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