Benefits of chaining LLMs - amosproj/amos2025ss04-ai-driven-testing GitHub Wiki

Chaining LLMs for Improved Output

Overview

Chaining LLMs refers to the sequential or parallel use of multiple AI models, where the output of one model serves as the input for another. This approach leverages specialized models for subtasks, enabling more complex workflows than single-model inference. Below, we explore the benefits, industry applications, and technical feasibility of this paradigm.

1. Industry Adoption of LLM Chaining

Several companies/tools already use chaining or agentic workflows:

Product/Tool	Use Case	Source
GitHub Copilot	Agentic coding workflows with feedback loops	[1]
Cursor/Root Code	Multi-agent collaboration for code generation	[2]
Google Gemini 2.5	Handles long-context tasks via chained reasoning	[3]
LangChain	General-purpose LLM orchestration framework	[4]
AutoGen	Multi-agent conversational workflows	[5]

Key Insight: Chaining is already a proven pattern in AI-assisted coding tools.

2. Benefits of Chaining LLMs

a) Quality Improvement

Divide-and-conquer strategy: Breaking tasks into subtasks (e.g., code understanding → test generation → error fixing) reduces the cognitive load on individual models.
Feedback loops: Iterative refinement (e.g., compiling/running tests and fixing errors) ensures higher-quality outputs.
Specialization: Different dedicated large language models for specific tasks (e.g., planning, code generation, error analysis) outperform general-purpose models.

b) Parallelization and Scalability

Parallelizable subtasks (e.g., generating independent test cases) speed up workflows.

c) Context Management

Smaller context windows per model reduce token costs and avoid overwhelming LLMs with irrelevant data.

d) Measurable Outcomes

Mutation coverage increases from 0% (naive tests) to >80% with chained agents.

3. Technical Feasibility

a) Workflow Overview

graph TD
    A[("fa:fa-code Input Code")] --> B[/"Test Writer LLM"/]
    B --> C[("fa:fa-code Test Code")]
    C --> D{{"Compiler"}}
    D --> E{Compiles?}
    E -- No --> F[/"Error Solver LLM"/]
    F --> C
    E -- Yes --> G{{"Test Runner"}}
    G --> H{Tests Pass?}
    H -- No --> F
    H -- Yes --> I[("fa:fa-code Final Test Code")]

b) Key Components

Context Management
- Repository maps: Extract class/method signatures via tools like TreeSitter (6) to limit context size.
- Retrieval-augmented generation (RAG): For large codebases, use vector databases to retrieve relevant snippets.
  - The overhead of this architecture is disproportionate for smaller/mid-size codebases.
Orchestration Layer
- Manages model calls, parallelization, and error handling (e.g., Python-based CLI).
- Docker containers isolate test execution.
Error Handling
- Feedback Loops: Compiler and test runner logs guide error-solving agents.
- Fix: Patches are applied incrementally rather than regenerating entire files.

c) Parallel approach

The workflow can be significantly accelerated through parallel processing of independent test scenarios:

Key Parallelization Features:

Test Planning Phase: Initial Test Planner LLM identifies separable test scenarios
Independent Pipelines: Each parallel branch handles:
- Test generation for specific methods/classes
- Dedicated compilation environment
- Isolated test execution
- Self-contained error correction
Dynamic Resource Allocation: Pipelines can scale across: Multiple CPU cores, GPU partitions or distributed cloud workers

Performance Characteristics:

Sequential Execution: O(n × t)   # n scenarios × avg processing time
Parallel Execution:  O(t + logn) # Longest pipeline + merging overhead

graph TD
    A[("fa:fa-code Input Code")] --> B[/"Test Planner LLM"/]
    B --> C1[/"Test Writer LLM<br>(Pipeline 1)"/]
    B --> C2[/"Test Writer LLM<br>(Pipeline 2)"/]
    B --> Cn[/"Test Writer LLM<br>(Pipeline N)"/]
    
    subgraph Parallel Pipelines
    C1 --> T1[("fa:fa-code Test Code Suite 1")]
    C2 --> T2[("fa:fa-code Test Code Suite 2")]
    Cn --> Tn[("fa:fa-code Test Code Suite N")]
    
    T1 --> D1{{"Compiler 1"}}
    T2 --> D2{{"Compiler 2"}}
    Tn --> Dn{{"Compiler N"}}
    
    D1 --> E1{Compiles?}
    D2 --> E2{Compiles?}
    Dn --> En{Compiles?}
    
    E1 -- No --> F1[/"Error Solver LLM<br>(Pipeline 1)"/]
    E2 -- No --> F2[/"Error Solver LLM<br>(Pipeline 2)"/]
    En -- No --> Fn[/"Error Solver LLM<br>(Pipeline N)"/]
    
    F1 --> T1
    F2 --> T2
    Fn --> Tn
    
    E1 -- Yes --> G1{{"Test Runner 1"}}
    E2 -- Yes --> G2{{"Test Runner 2"}}
    En -- Yes --> Gn{{"Test Runner N"}}
    
    G1 --> H1{Tests Pass?}
    G2 --> H2{Tests Pass?}
    Gn --> Hn{Tests Pass?}
    
    H1 -- No --> F1
    H2 -- No --> F2
    Hn -- No --> Fn
    end

    H1 -- Yes --> I[/"Test Merger LLM"/]
    H2 -- Yes --> I
    Hn -- Yes --> I
    
    I --> J[("fa:fa-code Final Test Suite")]
    J --> K(("fa:fa-check Done"))

This parallel approach maintains all quality benefits of chained LLMs while dramatically reducing total processing time through concurrent execution of independent test generation pipelines.

Challenges and Considerations

Cost: Chaining multiple commercial LLMs can be expensive. So we should only use locally executable lightweight LLMs or host them on our own hardware so that the data is not passed on to third-party providers and the costs are kept low.
Latency: Feedback loops increase execution time. Therefore, this process should be triggered once by the user, then take a certain amount of time and report back with compilable working tests. In the meantime, the user can do something else.
Model Compatibility: Output formats (e.g., patches vs. full files) must align across models.
Security: Generated code may introduce vulnerabilities or infinite loops. Isolation and timeouts are important here
Performance: Larger, newer models perfom better.

4. Conclusion

Advantages

✅ Higher quality outputs through iterative refinement
✅ Better handling of complex tasks
✅ Measurable improvements in test coverage

Challenges

⚠️ Increased complexity in orchestration
⚠️ Longer execution times (minutes/hours vs seconds)
⚠️ Higher computational costs

Chaining LLMs offers significant advantages for complex tasks like test generation, code migration, and workflow automation. While implementation requires careful orchestration and context management, the approach is already validated by industry tools. Future directions include extending chaining to integration testing, end-to-end testing, and multimodal workflows (e.g., combining code and visual inputs).