The Pre-Solving Phase: Why CrossChat Analyzes Your Question Before Answering
What is the pre-solving phase in CrossChat, how question decomposition works, and why this step improves answer relevance for complex queries.
You ask a question. The model answers immediately — fluently, confidently, with clear structure. Three minutes later you realize it was answering a different problem than the one you had in mind.
The biggest loss of AI answer quality does not happen during text generation. It happens earlier — during question interpretation. Complex questions carry implicit assumptions, ambiguous terms, and hidden sub-problems. The model typically does not reflect on them. It simply answers.
The pre-solving phase adds that reflection.
Claims Framework What this article claims: Decomposing a question, surfacing implicit assumptions, and selecting a technique before generating an answer improves output relevance for complex queries. What it is based on: Chain-of-thought reasoning principles (Wei et al., 2022) and ReAct (Yao et al., 2022), which show that structured reasoning before answering improves quality. CrossChat's implementation builds on these concepts. Where it simplifies: The article describes a planned CrossChat feature; real-world impact depends on implementation and question type. The benefit for simple queries is not claimed and is explicitly noted.
What the Pre-Solving Phase Does
The pre-solving phase analyzes your question before selecting a workflow and beginning to answer. It does not add delay — it adds structure.
It performs three steps:
1. Question decomposition. A compound problem is broken down into sub-questions. What is a known fact, what is uncertain, what is an empirical question, and what is a value judgment? This separation prevents the model from mixing different types of arguments — factual claims with preference-based recommendations.
2. Identification of implicit assumptions. Every question carries assumptions the author considers obvious. "Which model is best?" — best according to what criterion? Price, speed, accuracy, or suitability for a specific domain? Pre-solving surfaces these assumptions before the model chooses them on its own.
3. Technique recommendation. Based on the decomposition, the system recommends a suitable workflow. Factual question with uncertainty → cross-check across multiple models. Complex decision with multiple perspectives → multi-model debate or council. Creative task → different configuration. The technique is selected according to the structure of the problem, not arbitrarily.
Where the value is independent of any specific platform: these three steps can be performed manually before submitting a question to any AI tool. Pre-solving as a principle is transferable.
When to Use Pre-Solving — and When Not To
Pre-solving costs additional time and tokens. The investment pays off only when the question is sufficiently complex.
| Situation | Pre-solving? | Why | |-----------|-------------|-----| | Complex business decision | ✓ Yes | Multiple sub-problems, implicit criteria | | Analytical research | ✓ Yes | Hidden assumptions, need for evidence | | Strategic question | ✓ Yes | Values and facts must be separated | | Simple factual query | ✗ No | Overhead without benefit | | Quick term lookup | ✗ No | Overhead without benefit | | Creative generation | ✓ Optional | Depends on complexity of the brief |
Decision rule: if the phrasing of your question depends on context that is not explicit in the question itself — pre-solving helps. If the question has a clear answer that does not need decomposition — pre-solving is unnecessary.
How Pre-Solving Changes the Result: A Concrete Example
Question: "Should we outsource customer service?"
Without pre-solving: The model immediately compares advantages and disadvantages of outsourcing — generic arguments about costs and quality, without the context of your situation. The result sounds convincing but answers a generic question, not yours.
With pre-solving:
Step 1 — Decomposition: The question has a financial dimension (costs of internal vs. external service), a qualitative dimension (customer experience, resolution speed), a strategic dimension (process control, customer knowledge retention), and a contextual dimension (where does the current service actually fail?).
Step 2 — Identifying assumptions: "Outsource" — to whom, which part, for how long? What are the current pain points of the internal service? What makes customer service in your industry specific?
Step 3 — Technique recommendation: Multi-model debate, where different models play different roles — CFO (financial perspective), CX manager (customer experience), risk officer (strategic risk). Each role analyzes one dimension of the question.
The result is a structured analysis of three dimensions with clearly separated argument types — rather than a single persuasive but context-blind recommendation.
Frequently Asked Questions
Isn't it enough to clarify the question myself? Yes, but pre-solving identifies assumptions you cannot see — because they are obvious to you. "Outsource customer service" — you know what you mean. The model does not, and will substitute its own implicit context.
How long does pre-solving take? Typically one additional step before the main answer. Duration depends on the complexity of the question — simple problems complete quickly, complex strategic questions may require longer decomposition.
Can I do pre-solving manually without a tool? Yes. Before submitting a question, write out: what are the sub-questions, what assumptions does the question carry, what type of answer am I looking for (facts, recommendation, analysis of options). That is manual pre-solving.
How to Try It
The pre-solving phase is built into CrossChat workflows automatically — the system applies it to more complex questions and displays the decomposition results as part of the answer process. You can see how the system interpreted your question and correct it before receiving the final output.
If you prefer a manual approach, the three-step principle — decomposition → identifying assumptions → selecting a technique — works in any chat interface. You simply perform it explicitly yourself before submitting the main question.
Sources
- Yao, S. et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629. DOI: 10.48550/arXiv.2210.03629
- Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903. DOI: 10.48550/arXiv.2201.11903
Editorial History
Concept: Claude Code + Anthropic Sonnet 4.6 Version 1: Claude Code + Anthropic Sonnet 4.6 Version 2: Codex + GPT-5.2
Quality audit (2026-03-24, Claude Code + Claude Opus 4.6): added Claims Framework, verified sources, language polish.