RHODES Framework (RHODES)

What is the RHODES Framework?

RHODES is a six-component prompt engineering framework modeled on academic and scientific research methodology. The acronym stands for Research, Hypothesis, Objectives, Development, Execution, and Synthesis — a systematic sequence that mirrors how rigorous analysis is conducted in academic and professional research contexts.

• R — Research: Define the evidence to gather
• H — Hypothesis: State a testable proposition
• O — Objectives: Define specific research goals
• D — Development: Build the analytical framework
• E — Execution: Apply the framework to the evidence
• S — Synthesis: Summarize findings and draw conclusions

Unlike frameworks optimized for single-turn Q&A or structured advisory tasks, RHODES is designed for complex, multi-stage investigations where the quality of reasoning depends on following a disciplined methodology from initial evidence gathering through to synthesized conclusions.

The Hypothesis component is what makes RHODES distinctive: by requiring you to state a falsifiable proposition before the analysis begins, it forces clarity of purpose and creates a testable standard against which findings can be evaluated. This mirrors the scientific method and dramatically improves the rigor and usefulness of the output compared to open-ended research prompts.

RHODES is the most advanced of the common structured frameworks — it rewards users who invest time in all six components and is overkill for tasks that do not genuinely require a research methodology.

When to Use the RHODES Framework

How to Use the RHODES Framework

1. 1

   Research — Define the evidence to gather

   Specify the sources, timeframe, and type of evidence you want the model to draw from. Be specific about whether you want peer-reviewed studies, industry reports, case studies, or primary data. Setting clear research parameters prevents the model from defaulting to outdated or low-quality sources.

2. 2

   Hypothesis — State a testable proposition

   Formulate a clear, falsifiable hypothesis that the research will evaluate. This does not need to be a formal scientific hypothesis — it can be a working assumption, a proposed explanation, or a directional claim you want tested. The hypothesis creates the standard against which findings will be measured.

3. 3

   Objectives — Define specific research goals

   List 2-4 concrete objectives the analysis must achieve. Each objective should be specific and measurable: "Quantify X", "Compare A and B on Y dimension", "Identify the conditions under which Z holds." Objectives prevent the analysis from drifting into irrelevant territory.

4. 4

   Development — Build the analytical framework

   Describe the structure, model, or framework the model should construct as the basis for the analysis. This might be a taxonomy, a comparison matrix, a decision tree, or a theoretical model. Development produces the analytical tool that the Execution stage then applies to the evidence.

5. 5

   Execution — Apply the framework to the evidence

   Direct the model to apply the framework developed in the previous stage to the gathered evidence. Specify what to look for, how to handle contradictions or gaps, and what level of detail is expected in this analytical stage.

6. 6

   Synthesis — Summarize findings and draw conclusions

   Ask the model to consolidate the analysis into a clear summary of key findings, a verdict on the hypothesis, actionable implications, and an honest assessment of limitations. Synthesis transforms analysis into usable knowledge.

Prompt Examples

Research: Gather current evidence on the effectiveness of spaced repetition systems (SRS) in adult language acquisition. Focus on peer-reviewed studies published after 2018 and real-world data from applications like Anki and Duolingo.

Hypothesis: Spaced repetition significantly outperforms traditional massed practice (cramming) for long-term vocabulary retention in adult language learners, but the advantage diminishes for grammar acquisition.

Objectives: (1) Quantify the retention advantage of SRS over massed practice. (2) Assess whether the advantage holds equally for vocabulary vs. grammar. (3) Identify the optimal review interval for adult learners based on current evidence.

Development: Build a comparative analysis framework contrasting SRS and massed practice across retention rates, time investment, and learner adherence. Apply this framework to the gathered evidence, distinguishing vocabulary studies from grammar studies.

Execution: Analyze the evidence using the framework. Identify consistent findings, contradictions, and gaps. Note sample sizes, study durations, and target languages where relevant to assess generalizability.

Synthesis: Summarize key findings in 3-5 bullet points. State whether the hypothesis is supported, partially supported, or unsupported. Recommend practical SRS implementation guidelines for an adult learner beginning a new language. Note the most significant limitation of current evidence.

Research: Investigate the current state of retrieval-augmented generation (RAG) architectures for enterprise knowledge management. Draw on recent technical literature (2023-2025), major vendor implementations, and documented production case studies.

Hypothesis: Naive RAG implementations (single-stage retrieval + generation) are insufficient for enterprise knowledge management at scale, and multi-stage or agentic RAG architectures are necessary to meet accuracy and latency requirements above a certain knowledge base size threshold.

Objectives: (1) Define the performance boundaries of naive RAG. (2) Identify the leading multi-stage and agentic RAG architectures. (3) Assess the practical trade-offs between retrieval accuracy, latency, and implementation complexity.

Development: Construct a tiered architecture taxonomy covering naive RAG, advanced RAG (re-ranking, query decomposition, hybrid retrieval), and agentic RAG. For each tier, define the capability profile, the implementation complexity, and the scale at which it becomes necessary.

Execution: Apply the taxonomy to evaluate three documented enterprise use cases (minimum). Assess where each sits in the taxonomy and whether the architecture matches the actual requirements. Identify patterns in architecture selection across use cases.

Synthesis: Provide a decision framework for architecture selection based on knowledge base size, query complexity, and latency requirements. State whether the hypothesis is confirmed. Identify the most critical open research question for practitioners deploying RAG in production.

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