ChainForge: Visual Toolkit for LLM Experiments

• ChainForge is an open-source visual toolkit for prompt engineering, offering a graphical dataflow environment for systematic LLM comparisons and modular experimentation.
• Its node-based architecture supports inputs, generators, evaluators, and visualizers, enabling prompt chaining and combinatorial query management for robust model evaluation.
• The integration of ChainBuddy automates workflow generation from natural language, reducing user workload while maintaining experimental rigor.

ChainForge is an open-source visual toolkit for prompt engineering and on-demand hypothesis testing of text-generating LLMs. Developed to bridge the gap between low-level, code-intensive LLM experimentation and highly restrictive, domain-specific tools, ChainForge provides a graphical environment that enables systematic model and prompt comparison, evaluation, and pipeline prototyping for both researchers and practitioners (Arawjo et al., 2023).

1. System Architecture and Design Principles

ChainForge employs a node-based, graphical dataflow paradigm, in which users construct workflows ("flows") by connecting a set of typed nodes. These nodes encapsulate inputs (e.g., text fields, tabular data), prompt templates, LLMs, evaluators, and visualizers. Node types include:

• Inputs: Single/multiline text fields, tabular or CSV data nodes (with variable support).
• Generators: Prompt and chat turn nodes, supporting chaining and recursive variable expansion.
• Evaluators: Modules for automated evaluation using Python, JavaScript, LLM-based scoring, or rule-based logic.
• Visualizers: Components for tabular/list visualization, response inspection, and metric plotting.
• Miscellaneous: Comments and embedded inspectors.

Flows are visualized as directed acyclic graphs. Each edge represents data or control dependency between nodes. The graphical UI provides immediate visual transparency of all experiment logic, supporting transparency, rapid contiguity of operations, and collaborative sharing. Each node exposes controls for editing, viewing intermediate results, or swapping models/providers.

Core to its design, ChainForge implements combinatorial prompt and model queries. For a set of $P$ prompt permutations (from template variables or chaining), $M$ models, $N$ sampled responses per prompt, and $C$ chat histories, the total number of LLM queries is: $$\text{Number of queries} = P \times M \times N \times \max(1, C)$$ This combinatorial enumeration is managed automatically, supporting systematic ablation or robustness studies without user-side code (Arawjo et al., 2023).

2. Supported Tasks and Core Functionality

The platform was designed to address four principal axes of LLM experimentation:

Design Goal	Implementation Features
Model Selection (D1)	Query multiple models, direct response comparison
Prompt Template Design (D2)	Template prompts w/ variables, hierarchically chain templates
Systematic Evaluation (D3)	Configure evaluators, visualize distributions and metrics
Improvisation (D4)	Reactive flow, on-the-fly adjustment, non-linear branching

Model selection workflows are implemented by connecting prompt templates to multiple model nodes, allowing for instantaneous side-by-side comparative inspection using the response inspector (grouped list or table visualization modes). Prompt engineering is facilitated by variable injection and template chaining, enabling the recursive composition of hierarchical prompts. Robustness or bias audits are accomplished by integrating ground-truth data (tabular), systematic variation of input variables, and bespoke evaluators (code or LLM-based) analyzing outputs according to arbitrary metrics.

Scalability is ensured via response caching, with only new query permutations submitted to backend LLM providers upon workflow mutations. The system supports rapid flow export/import via files or web links, enabling reproducible and collaborative experiment sharing.

3. Evaluation Modalities and User Study Insights

Empirical research identified three characteristic user interaction modes:

1. Opportunistic Exploration: Rapid, informal trial of prompts, inputs, or models, often using small examples for failure mode discovery (e.g., adversarial testing, preliminary bias checks), leveraging ChainForge's fast iteration and multi-model layout.
2. Limited Evaluation: Introduction of basic, often coarse automated evaluators and visual summaries, enabling systematic assessment but at small to moderate scale (e.g., format correctness, success/failure markers).
3. Iterative Refinement: Repeated adjustment of prompts, input sets, or evaluators to optimize behavior, error coverage, or robustness. Users may scale up datasets (e.g., via spreadsheet import), swap models, or develop more complex branching flows, cycling back to exploration as new hypotheses emerge.

In-lab studies with 22 participants (technical and non-programmers) found ChainForge enabled high-perceived efficiency (4.19/5), with participants citing workflow transparency and ease of model/prompt comparison. Challenges included scaling to large systematic evaluations and discoverability of certain features (e.g., deep template chaining, metavariables).

Interview studies with real-world users highlighted extensibility as a differentiator. ChainForge was repurposed for LLM-powered data processing pipeline prototyping beyond traditional prompt engineering (e.g., tabular data transformations, information extraction workflows), underscoring the value of its visual, modular logic. Central user needs included enhanced data import/export and improved discoverability of advanced affordances.

4. Systematic Comparison and Visualization

Systematic experimentation is a core affordance. The inspector node supports grouped and tabular layouts—responses to each prompt variant can be viewed side-by-side across all LLMs. Visualization nodes enable aggregation and charting of evaluation metrics (e.g., Boolean or scalar scores plotted by prompt template or model). This architecture allows at-a-glance comparison of:

• LLM output diversity and failure modes given prompt or input perturbations,
• Robustness to prompt injection or adversarial content,
• Subtle model differences in following instructions, bias, or error patterns,
• Effects of template chaining or variable expansion on model performance.

Evaluation nodes can incorporate Python or JavaScript for metric definition, as well as LLM-based evaluators leveraging meta-prompts. Systematic model and prompt comparison can thus proceed without scripting boilerplate or code maintenance, while maintaining experimental rigor.

5. Integration of Workflow Generation Assistants: ChainBuddy

ChainBuddy, introduced as an agentic, conversational assistant embedded within ChainForge, addresses the "blank page problem" prevalent in LLM experimentation (Zhang et al., 20 Sep 2024). Users initiate interaction in a chat widget, describing desired evaluation or pipeline logic in natural language. The assistant performs structured requirements elicitation (posing up to three clarifying questions), then delegates to a modular backend agent system:

• Planner Agent: Partitions the user's aim into subtasks mapped to ChainForge nodes.
• Task-Specific Sub-agents: Generate specialized node definitions.
• Connection Agents: Link nodes, deduce layout.
• Reviewer Agent (optional): Validates the proposed flow (disabled in user studies for latency).

The result is an executable, editable JSON flow visualized in ChainForge, providing an immediate starting structure. For instance, a request to "explore persona effects on math problem answering" yields input nodes for sample personas, math problem nodes, templated prompt nodes, model queries, and evaluators (e.g., Python scripts checking for output correctness like $\sqrt{\pi}$).

Mixed-method user studies demonstrated that ChainBuddy reduces mental and physical workload (NASA-TLX: $\beta_{\text{mental}} = -0.91, p = 0.01$; $\beta_{\text{physical}} = -1.08, p = 0.01$), increases confidence ($\beta = 0.5, p = 0.04$), and substantially raises the success rate (11/12 participants constructed correct flows with ChainBuddy, $\sim4/12$ in manual mode). Users more frequently created a wider variety of node types ($\beta = 0.583, t = 4.04, p = 0.003$).

A notable phenomenon was the mismatch between subjective and objective performance: users often felt equally successful in both manual and assisted modes, while independent expert assessment found significantly higher flow quality in the assisted condition. This aligns with the Dunning-Kruger effect, suggesting that automation can mask skill gaps, encourage over-reliance, and restructure user problem-solving even in subsequent manual efforts (Zhang et al., 20 Sep 2024).

6. Broader Impact and Design Implications

ChainForge demonstrates the viability and utility of visual, modular tooling in prompt engineering, LLM evaluation, and LLM-powered data pipeline prototyping. Its open-source extensibility has enabled unforeseen applications, particularly in data processing and multi-model experimentation. Integration of assistants like ChainBuddy highlights both performance gains and the risk of automation complacency—where users may uncritically follow or anchor to AI-generated designs, missing errors without realizing proficiency gaps.

Recommended design directions for future workflow generation tools include:

• Combining subjective (usability, satisfaction) and objective (task correctness) metrics in evaluation,
• Scaffolding alternatives and promoting critical engagement rather than prescribing single solutions,
• Providing transparent rationales for pipeline structures,
• Balancing clarification depth to reduce question fatigue,
• Supporting iterative editing with assistant involvement,
• Empowering agency by surfacing system confidence and completeness.

A plausible implication is that visual prompt engineering toolkits, especially when agentically augmented, can amplify user exploration, systematic evaluation, and collaborative work, provided that transparency and critical engagement are designed-in to mitigate over-reliance.

7. Summary Table: Relationship Between Design Goal and Features

Design Goal	Implementation Features
Model selection	Querying multiple models, side-by-side/grouped response plots
Prompt template design	Variable templates, hierarchical chaining, visualization
Systematic evaluation	Automated evaluators, meta-variable referencing, visualization
Improvisation and iteration	Responsive flow, caching, non-linear branching, flow edits

ChainForge constitutes a comprehensive, extensible platform for both exploratory and systematic engineering of LLM prompts and behaviors, while the integration of agentic assistants such as ChainBuddy marks a significant advance in workflow scaffolding, with both efficacy and cautionary lessons for the evolution of LLMOps tooling (Arawjo et al., 2023, Zhang et al., 20 Sep 2024).