ChainBuddy: AI Assistant for LLM Evaluation

• ChainBuddy is an agent-based AI assistant in ChainForge that auto-generates and refines workflow graphs for LLM evaluation, addressing prompt engineering challenges.
• It utilizes a multi-agent architecture to interactively elicit requirements and construct complete, editable evaluation pipelines, reducing cognitive load and enhancing accuracy.
• Empirical studies reveal that ChainBuddy significantly lowers user workload and improves pipeline correctness, though it may induce over-reliance due to a Dunning-Kruger effect.

ChainBuddy is an agent-based AI assistant integrated into the ChainForge environment for generating and refining LLM evaluation pipelines. Its design addresses the challenge of scaffolded creation and assessment of LLM workflows, tackling the “blank page problem” common in prompt engineering and LLMOps contexts. It achieves this by interactively eliciting requirements through chat and auto-generating complete, editable workflow graphs tailored to user-defined evaluative tasks. ChainBuddy has been systematically evaluated both in terms of user experience and objective task performance, revealing design trade-offs and implications for workflow assistant systems.

1. System Objective and Context

ChainBuddy is designed to facilitate the planning and evaluation of LLM behaviors for user-specified tasks through concrete workflow suggestions embedded within ChainForge, a visual prompt and pipeline authoring platform. The system’s core purpose is to mitigate barriers in initiating and structuring multi-stage LLM evaluations, including prompt comparisons, scenario audits, and output analysis. By automating the draft generation of ChainForge-compatible flows from minimal user input (single prompt or brief conversational exchange), ChainBuddy aims to reduce cognitive load, increase pipeline quality, and streamline the “LLMOps” process for both novice and experienced practitioners.

The primary user need it addresses is workflow bootstrapping—moving users from open-ended objectives to actionable, evaluative structures that can assess and compare LLM outputs across various axes (prompt templates, model choice, input variations, evaluation criteria).

2. Architecture and Agentic Workflow Generation

ChainBuddy operates as a multi-agent system, leveraging stateful LLM backend frameworks (LangGraph) and advanced LLMs (Claude 3.5 Sonnet for frontend interaction, GPT-4o for backend planning and subagent execution).

The assistant’s workflow comprises:

• Requirement Elicitation Agent: Engages users in a chat-based dialogue or structured Q&A, extracting primary goals, evaluation desiderata, and clarifying ambiguities via adaptive question sequences. This interaction happens inline in ChainForge’s UI.
• Planner Agent: Constructs a pipeline graph that captures the sequence of operations required for the desired evaluation, informed by background knowledge of all available ChainForge nodes and their compatibility.
• Node Creation Agents: Populate each node (e.g., data source, prompt template, LLM/model object, evaluation module) with contextually relevant parameters.
• Connection Agents: Link generated nodes into a coherent, executable workflow graph, specifying node order and data dependencies in ChainForge JSON format.
• Reviewer (optional): May be invoked to check the coherence and task-alignment of the proposed pipeline, though it was disabled during the main paper to avoid added latency.

The output is a fully instantiated, visual ChainForge flow, containing input data staging, prompt and model comparisons, and evaluation metrics (including Python/LLM-based scorers).

3. User Workflow and Interaction Patterns

The intended user interaction model begins with a natural language specification of the workflow’s goal (e.g., “Compare how three prompt styles impact math accuracy in GPT-4 and Claude 3.5”). ChainBuddy then guides requirements elaboration through up to three rounds of form-based and conversational clarification, ensuring the system resolves ambiguities and gathers missing details (e.g., specific evaluation metrics, data formats).

Once requirement gathering is complete, the user triggers auto-generation; after a short computational delay, ChainBuddy instantiates and displays the resulting flow within ChainForge. Users may then refine, extend, or edit this automatically created pipeline using ChainForge’s graphical tools, facilitating iterative improvement and exploration. The process is designed to lower initial activation energy and scaffold sound workflow design, making advanced tasks more accessible.

A visual walk-through of this workflow is depicted in the figures “generation_process.pdf” (conversation flow and pipeline instantiation) and “system_arc3.png” (multi-agent backend architecture).

4. Empirical Evaluation: User Study Methodology and Outcomes

A within-subjects, counterbalanced user paper assessed the impact of ChainBuddy’s assistance compared to manual pipeline creation in ChainForge. Twelve technically sophisticated participants (ages 18–34, mostly CS/engineering, moderate-to-high LLM and Python fluency) completed two LLMOps tasks involving structured workflow construction and prompt evaluation.

Key experimental features:

• Quantitative Measures: NASA-TLX mental and physical workload, System Usability Scale (SUS) for confidence and complexity, objective task correctness (workflow met all specified functional benchmarks), detailed node usage/frequency, and self-reported time to completion.
• Qualitative Measures: Thematic interviews, exploration logs, and analysis of workflow strategies.

Key Results

• Workload Reduction and Confidence: Participants using ChainBuddy reported significantly lower mental demand ($\beta=-0.91$, $p=0.01$) and physical demand ($\beta=-1.08$, $p=0.01$), and higher confidence ($\beta=0.5$, $p=0.04$) relative to manual pipeline construction.
• Objective Performance: With AI assistance, 11/12 participants constructed correct and functional flows (explicit prompt comparison, evaluator node inclusion, and template chaining as required). Only ~1/3 achieved this in the control condition.
• Node Complexity: Assistant-enabled workflows consistently included more evaluator and advanced node types, whereas manual users often omitted these.
• Task Completion Time: No statistically significant effect on self-reported time, though some evidence suggested participants declared completion earlier with AI in certain scenarios.
• Subjective-Objective Dissociation: Despite large objective accuracy gaps, users’ self-reported success was similar across both conditions. This stark disparity illustrates a pronounced Dunning-Kruger effect—users overestimate their effectiveness absent automated feedback or external expertise.
• Qualitative Themes: Users valued ChainBuddy as a “springboard,” overcoming initiation barriers and enabling rapid iteration, but some evidenced anchoring—retaining similar structure in subsequent workflows even without AI guidance.

Representative usage cases included auditing LLMs for persona-conditioned reasoning, prompt engineering for math accuracy, model output translation, SPARQL query generation, and evaluation of sensitive content handling.

5. Failure Modes, Over-Reliance, and Design Implications

The primary finding beyond raw performance is the risk of overconfidence and potential over-reliance on AI-generated scaffolds. The subjectively high confidence and parity in perceived task success—contradicted by external expert and objective scoring—underscore the risk that users may accept AI-generated or manually built workflows as correct even when incomplete or technically invalid.

Design recommendations articulated by the paper include:

1. Incorporation of Objective Validators: Assistants should embed automated correctness checks, peer/expert review triggers, or self-assessment prompts to ensure actual solution quality.
2. Antidotes to Over-Reliance: Providing multiple variant drafts, encouraging explicit reflection, and flagging under-specified flows can reduce learned inaccuracy.
3. Balanced Requirement Gathering: Structured elicitation should avoid over-questioning; adaptive logic to determine sufficiency is preferable.
4. Combining Criteria in Evaluation: System and interface design should blend both subjective experience and rigorous externally validated measures.
5. Progressive Ownership: Systems should enable users to quickly progress from AI-provided starter flows toward more bespoke, refined, or complex solutions with integrated rationale explanations.
6. Detection of "Unknown Unknowns": Interfaces should highlight parts of the workflow with the greatest uncertainty or mismatch to known good patterns, guiding users to review or request further help.
7. Transparent Decision Tracing: Exposing the agent reasoning for workflow structure helps build trust and equips users to challenge, adapt, or correct the assistant’s outputs.

These findings have broad consequences for the responsible deployment of AI-augmented productivity tools in technical workflows, emphasizing the necessity to pair automation with mechanisms to surface and address “epistemic blind spots.”

6. Quantitative Summary of Key Results

Measure	Assistant	Control	Statistically Sig.?
Mental Demand (NASA TLX)	Lower	Higher	Yes ($p=0.01$)
Physical Demand (NASA TLX)	Lower	Higher	Yes ($p=0.01$)
User Confidence (SUS)	Higher	Lower	Yes ($p=0.04$)
Objective Flow Correctness	11/12	~1/3	Yes
Evaluator Node Inclusion	Most users	One user	Yes
Self-reported Task Success	Parity	Parity	No (Mismatch)
Subjective Time to Completion	No effect	No effect	No

This table synthesizes the key quantitative findings—ChainBuddy improves task correctness and user experience objectively, but subjective perceptions of success do not track true performance.

7. Conclusions and Broader Implications

ChainBuddy demonstrates the impact of deeply integrated, agentic AI assistance for LLM pipeline bootstrapping, substantially increasing both evaluation quality and user confidence while reducing workload. However, the paper identifies critical design risks: subjective impressions can lag objective progress, especially for non-expert users in “open-ended ML” workflows. Future systems in this class should embed trustworthy guardrails, validation, and paths for users to acquire both rapid head starts and nuanced understanding of workflow robustness, mitigating epistemic and operational risks associated with automated assistance.