docs(README): rewrite dataset READMEs for clarity and alignment

README.md

@@ -46,37 +46,22 @@ size_categories:
 - n<1K
 ---
 
-
 # dia-intent-sequencer-robot-arm-dataset
 
-This dataset provides natural language instructions paired with structured DSL function call trees using the **DIA intent sequencer** format. It is designed to train and evaluate models that control a robotic arm managing an inventory of screws.
+This dataset provides natural language instructions paired with structured DSL function call trees using the **DIA intent sequencer** format. It is used to train and evaluate a demo model that controls a custom robotic arm managing an inventory of screws.
 
 Unlike synthetic datasets, this one was **authored by hand** to reflect real-world, grounded use cases. It illustrates:
 
-- 🛠 Mapping user intent to tool calls  
-- 🔄 Dynamically resolving missing parameters using runtime-accessible resources  
-- 🤖 Interactively clarifying ambiguous input and handling runtime errors through dialogue
-
-The primary goal of this dataset is to serve as a **demonstration and testbed**, showcasing a model's ability to engage with users to resolve incomplete or ambiguous inputs and to recover from runtime errors during task execution.
-
----
+- 🛠 Mapping user intent to tool calls
+- 🔄 Attempting to resolve missing parameters using runtime-accessible resources
+- 🤖 Attempting to clarify ambiguous input and to handle runtime errors through dialogue
 
-## 🧩 Related Datasets
+The goal of this dataset is to serve as a **demonstration and testbed**, showcasing a model's ability to engage with users in an attempt to resolve incomplete or ambiguous inputs, and to recover from runtime errors during task execution when possible.
 
 This dataset is part of a broader collection of **`DIA`** datasets, each demonstrating different capabilities of intent sequencing and tool-based reasoning:
 
 - [`a6188466/dia-intent-sequencer-calculator-dataset`](https://huggingface.co/datasets/a6188466/dia-intent-sequencer-calculator-dataset)  
-  Designed for training a model to translate symbolic math instructions into structured function call trees. It illustrates:
-  - Structural reasoning over nested, composable intent calls  
-  - Generalization across randomized function and parameter names  
-  - Pure function execution with no runtime queries or clarification steps
-
 - [`a6188466/dia-intent-sequencer-calendar-dataset`](https://huggingface.co/datasets/a6188466/dia-intent-sequencer-calendar-dataset)  
-  Designed for training a task/reminder scheduling agent. It illustrates:
-  - Mapping of user intent to tool calling  
-  - Nesting of intents
-  - Dynamic resolution of missing information from runtime-accessible resources  
-  - Interactive clarification of missing or conflicting information via user interaction
 
 ---
 
@@ -84,11 +69,11 @@ This dataset is part of a broader collection of **`DIA`** datasets, each demonst
 
 The dataset uses a **wide format** with the following columns:
 
-- `system` — the system prompt defining the model's task  
-- `in` — a natural language instruction related to managing or retrieving screws (e.g. `"retrieve 3 screws that are 10mm long"`)
-- `out` — a structured DSL expression that encodes tool calls with function/argument mapping (e.g. `"retrieve_screw(count=3, length=10)`)
+- `system` — the system prompt that defines the model's task
+- `in` — a natural language instruction related to managing or retrieving screws (e.g., `"retrieve 3 screws that are 10mm long"`)  
+- `out` — a structured DSL expression encoding tool calls with function and argument mappings (e.g., `retrieve_screw(count=3, length=10)`)
 
-Each entry is structured as:
+Each entry follows the structure:
 
 ```
 system → in → out
@@ -98,32 +83,25 @@ system → in → out
 
 ## 📋 System Prompt Format
 
-The `system` prompt is a structured instruction that tells the model to translate the user input into one or more **atomic DIA DSL function calls** based on a list of available functions (tools) that the robotic arm can perform, such as `retrieve_screw`, `initialize`, and others.
-
-The [DIA LLM invocation strategy](https://github.com/gh9869827/fifo-dev-dsl/tree/main/fifo_dev_dsl/dia#-llm-invocation-strategy) documents the various prompts used by the LoRA adapter trained with this dataset. This dataset includes examples of the following system prompts:
+The `system` prompt is a structured instruction that tells the model to translate the user input into one or more **atomic DIA DSL function calls** based on a list of available functions (tools) that the demo robotic arm can perform, such as `retrieve_screw`, `initialize`, and others.
 
-- `system_prompt_intent_sequencer`  
-- `system_prompt_slot_resolver`  
-- `system_prompt_error_resolver`  
+The [DIA LLM invocation strategy](https://github.com/gh9869827/fifo-dev-dsl/tree/main/fifo_dev_dsl/dia#-llm-invocation-strategy) documents the various prompts used by the demo LoRA adapter trained with this demo dataset.
 
-Because the robotic arm operates in a real-world environment, the model is expected to handle missing or incomplete information using `QUERY_FILL`, `QUERY_USER`, and other runtime-aware operations. These enable the model to:
+Because the demo robotic arm operates in a real-world environment, the model is expected to handle missing or incomplete information using `QUERY_FILL`, `QUERY_USER`, and other runtime-aware operations, for example, to:
 
 - Query the inventory for relevant values  
 - Propagate values through chained tool calls  
-- Interactively clarify incomplete or conflicting user input  
+- Attempt to clarify incomplete or conflicting user input through interaction
 
-> 💡 Example: A user might say "Give me one of the longest screws." The model should use `QUERY_FILL(...)` to retrieve the appropriate length at runtime.
+> 💡 Example: A user might say, "Give me one of the longest screws." The model will attempt to use `QUERY_FILL(...)` to retrieve the appropriate length at runtime.
 
-## 🧠 DIA DSL Project
+---
 
-The DSL used in this dataset is defined in the [DIA module](https://github.com/gh9869827/fifo-dev-dsl/tree/main/fifo_dev_dsl/dia). It is a DSL for interactive agents that translates user intent into tool calls, resolving missing information and runtime errors through user interaction or by querying contextual information available at runtime.
+## 🧠 DIA DSL Project
 
-- 🧠 **Structured intent-to-action mapping** for precise and secure execution  
-- 🧩 **Composable function calls** with nesting and multi-step workflows  
-- 🔄 **Interactive resolution** of missing parameters via queries or user input  
-- 🤖 **Built-in runtime recovery mechanisms** for handling runtime errors
+The DSL used in this dataset is defined in the [DIA module](https://github.com/gh9869827/fifo-dev-dsl/tree/main/fifo_dev_dsl/dia).
 
-For a complete example of how this model can be used in a robotic arm context, see [`robot_arm.py`](https://github.com/gh9869827/fifo-dev-dsl/blob/main/fifo_dev_dsl/dia/demo/robot_arm.py).
+For an example of a demo model built with this **demo dataset**, see [`robot_arm.py`](https://github.com/gh9869827/fifo-dev-dsl/blob/main/fifo_dev_dsl/dia/demo/robot_arm.py).
 
 ---
 
@@ -138,17 +116,24 @@ print(dataset_dict["train"])
 print(dataset_dict["validation"])
 ```
 
-This dataset is ideal for training `DIA` models that interact with both the user and the runtime environment. It supports use cases involving clarification, error handling, and dynamic value resolution.
-
 ---
 
 ## 🔧 Upload & Editing Tools
 
-Uploaded via [`fifo-tool-datasets`](https://github.com/gh9869827/fifo-tool-datasets) using the `dsl` adapter.  
+Uploaded via [`fifo-tool-datasets`](https://github.com/gh9869827/fifo-tool-datasets) using the `dsl` adapter.
+
 You can edit or extend the dataset using its `.dat` format and CLI tools.
 
 ---
 
+## ⚠️ Limitations
+
+This dataset was created to test intent sequencing using the DIA DSL with a custom robotic arm I built for experimentation. It focuses on a limited set of custom functions specific to that setup and serves as a simple, controlled example. It's not meant to cover the full range of possible user inputs, tasks, or edge cases.
+
+This dataset is provided as-is, without any warranties, express or implied. The authors and contributors assume no responsibility for its accuracy or suitability for any purpose.
+
+---
+
 ## 🪪 License
 
 This dataset is licensed under **CC BY 4.0**. See [LICENSE](LICENSE) for details.