diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
new file mode 100644
index 00000000..0d8cb6fc
--- /dev/null
+++ b/.github/copilot-instructions.md
@@ -0,0 +1,140 @@
+# PyRenew: Multi-signal Bayesian Renewal Modeling
+
+PyRenew is a Python package for simulation and statistical inference of epidemiological models using JAX and NumPyro, emphasizing renewal models for infectious disease forecasting and analysis.
+
+Always reference these instructions first and fallback to additional search or context gathering only when you encounter unexpected information that does not match the info here.
+
+## Working Effectively
+
+### Prerequisites and Installation
+- **CRITICAL**: Install `uv` package manager first: `pip install uv`
+- **Python version requirement**: Python 3.13+ (as specified in pyproject.toml)
+- Install development environment: `uv sync --extra dev`
+- Install test dependencies: `uv sync --extra test` 
+- Install documentation dependencies: `uv sync --extra docs`
+- Install ALL dependencies at once: `uv sync --all-extras`
+
+### Build and Test Process
+- **NEVER CANCEL**: Test suite takes ~2.5 minutes. ALWAYS set timeout to 5+ minutes for pytest commands.
+- **NEVER CANCEL**: Documentation build takes ~11 seconds, but first-time dependency installation takes 1-2 minutes.
+- Test installation: `uv sync --extra dev` -- takes ~1 minute on first run, ~0.3 seconds on subsequent runs
+- Run tests: `uv run pytest --mpl --mpl-default-tolerance=10` -- takes 2.5 minutes. NEVER CANCEL.
+- Run tests with coverage: `uv run pytest --mpl --mpl-default-tolerance=10 --cov=pyrenew --cov-report term --cov-report xml`
+
+### Documentation
+- **Requires Quarto CLI**: Install from https://github.com/quarto-dev/quarto-cli/releases
+- Build documentation: `cd docs && uv run make html` -- takes ~11 seconds
+- Documentation source: `docs/source/`
+- Tutorials: Quarto (.qmd) files in `docs/source/tutorials/`
+- API reference: Auto-generated from docstrings
+
+### Pre-commit and Code Quality
+- Install pre-commit manually: `uv pip install pre-commit` (not included in project dependencies)
+- Setup hooks: `uv run pre-commit install`
+- **NOTE**: Pre-commit is NOT included in the dev dependencies by design
+- Linting tools used: ruff (formatting and linting), numpydoc-validation, secret detection, typos checking
+
+### Key Commands Summary
+```bash
+# Installation (choose one based on needs)
+uv sync --extra dev        # Development dependencies only
+uv sync --extra test       # Test dependencies only  
+uv sync --extra docs       # Documentation dependencies only
+uv sync --all-extras       # All dependencies (recommended for full development)
+
+# Testing - NEVER CANCEL, takes ~2.5 minutes
+uv run pytest --mpl --mpl-default-tolerance=10
+
+# Documentation build
+cd docs && uv run make html
+
+# Basic Python module test
+uv run python -c "import pyrenew; print('PyRenew imported successfully')"
+```
+
+## Validation Scenarios
+
+### After Making Code Changes
+1. **ALWAYS** test import: `uv run python -c "import pyrenew; print('Import successful')"`
+2. **ALWAYS** run affected tests: `uv run pytest test/test_[relevant_module].py -v`
+3. **ALWAYS** run full test suite before committing: `uv run pytest --mpl --mpl-default-tolerance=10` -- NEVER CANCEL, takes 2.5 minutes
+4. **Check code style**: Run ruff formatting (if pre-commit installed)
+
+### For Documentation Changes
+1. **ALWAYS** rebuild docs: `cd docs && uv run make html`
+2. **Check tutorials**: Ensure .qmd files in tutorials/ render correctly
+3. **Verify API docs**: Check that module docstrings appear in generated docs
+
+### For New Features or Models
+1. **Test basic functionality**: Create minimal example using the new feature
+2. **Run relevant test modules**: Focus on affected areas (e.g., test_model.py for model changes)
+3. **Validate with real data**: Use example datasets in pyrenew.datasets if available
+
+## Repository Structure
+
+### Key Directories
+- `pyrenew/`: Main package source code
+  - `deterministic/`: Deterministic variables and components
+  - `distributions/`: Custom probability distributions
+  - `latent/`: Latent variable models
+  - `model/`: Complete model implementations
+  - `observation/`: Observation process models
+  - `process/`: Time series and stochastic processes
+  - `randomvariable/`: Random variable abstractions
+- `test/`: Comprehensive test suite (174 tests)
+- `docs/`: Sphinx documentation with Quarto tutorials
+- `hook_scripts/`: Pre-commit utility scripts
+
+### Important Files
+- `pyproject.toml`: Package configuration, dependencies, and tool settings
+- `Makefile`: Simplified commands (install, test targets)
+- `.pre-commit-config.yaml`: Code quality automation
+- `docs/source/conf.py`: Sphinx documentation configuration
+
+## Common Issues and Solutions
+
+### Build Issues
+- **Missing uv**: Install with `pip install uv`
+- **Wrong Python version**: Requires Python 3.13+, check with `python --version`
+- **Missing Quarto**: Required for documentation, install from GitHub releases
+- **Test failures**: Some tests require specific plot tolerances (--mpl flags)
+
+### Import Errors
+- **Missing dependencies**: Run `uv sync` with appropriate extras
+- **Path issues**: Ensure working directory is repository root
+- **JAX/NumPyro issues**: These are heavy dependencies; consider environment compatibility
+
+### Documentation Warnings
+- **Import warnings during doc build**: Normal for mocked heavy dependencies (JAX, NumPyro)
+- **Duplicate labels**: Expected for placeholder tutorial files
+
+## Development Workflow
+
+### Typical Development Session
+1. `uv sync --all-extras` (first time or after dependency changes)
+2. Make code changes
+3. `uv run python -c "import pyrenew"` (quick import test)
+4. `uv run pytest test/test_[relevant].py -v` (focused testing)
+5. `uv run pytest --mpl --mpl-default-tolerance=10` (full test suite - NEVER CANCEL)
+6. `cd docs && uv run make html` (if documentation changed)
+
+### Adding New Code
+- **Follow existing patterns**: Check similar modules for structure
+- **Add tests**: Every new feature should have corresponding tests
+- **Document thoroughly**: Use NumPy-style docstrings
+- **Consider tutorials**: Complex features may benefit from Quarto tutorial examples
+
+## Timing Expectations
+- **uv sync operations**: 0.3-2 minutes depending on cache state
+- **Test suite**: 2.5 minutes (NEVER CANCEL)
+- **Documentation build**: 11 seconds
+- **Simple imports**: <1 second
+- **Full environment setup**: 3-5 minutes for completely fresh install
+
+## CI/CD Integration
+- **GitHub Actions**: Uses `uv` for package management
+- **Test workflow**: Runs on Ubuntu with Python 3.13
+- **Documentation**: Auto-deployed to GitHub Pages
+- **Coverage**: Integrated with Codecov
+
+Remember: This is a scientific computing package dealing with epidemiological modeling. Precision and reproducibility are critical - always validate mathematical components thoroughly and never skip the full test suite.
\ No newline at end of file
diff --git a/docs/source/tutorials/getting_started.md b/docs/source/tutorials/getting_started.md
index 8e2d77b7..1dcbc84d 100644
--- a/docs/source/tutorials/getting_started.md
+++ b/docs/source/tutorials/getting_started.md
@@ -1,5 +1,97 @@
-## Placeholder file
- Please do not edit this file directly.
- This file is just a placeholder.
- For the source file, see:
- https://github.com/CDCgov/PyRenew/tree/main/docs/source/tutorials/getting_started.qmd
+# Getting started with pyrenew
+
+
+`pyrenew` is a flexible tool for simulating and making statistical
+inferences from epidemiologic models, with an emphasis on renewal
+models. Built on `numpyro`, `pyrenew` provides core components for model
+building and pre-defined models for processing various observational
+processes.
+
+## Prerequisites
+
+This tutorial assumes some pre-existing knowledge of infectious disease
+dynamics and Python programming. Before you dive in, we recommend:
+
+- Installing Python3 (use tools like
+  [pyenv](https://realpython.com/intro-to-pyenv/) or [compile and
+  install](https://ubuntuhandbook.org/index.php/2023/05/install-python-3-12-ubuntu/)
+  from the [release page](https://www.python.org/downloads/))
+- Familiarity with installing and loading modules in python, and with
+  virtual environment management (we recommend
+  [uv](https://docs.astral.sh/uv/))
+- Familiarity with the concept of a
+  [class](https://realpython.com/python-classes/) and
+  [metaclass](https://realpython.com/courses/python-metaclasses/) in
+  python
+- Familiarity with Bayesian inference, and a working understanding of
+  MCMC methods used to fit Bayesian models to data (some resources are
+  available
+  [here](https://mc-stan.org/docs/2_18/reference-manual/effective-sample-size-section.html),
+  and [here](https://xcelab.net/rm/))
+
+## Installing pyrenew
+
+You’ll need to install `pyrenew` using either uv or pip. To install
+`pyrenew` using `uv`, run the following command from within the
+directory containing the `pyrenew` project:
+
+``` bash
+uv sync
+```
+
+To install `pyrenew` using pip, run the following command:
+
+``` bash
+pip install git+https://github.com/CDCgov/PyRenew@main
+```
+
+## The fundamentals
+
+`pyrenew`’s core components are the metaclasses `RandomVariable` and
+`Model` (in Python, a *metaclass* is a class whose instances are also
+classes, where a *class* is a template for making objects). Within the
+`pyrenew` package, a `RandomVariable` is a quantity that models can
+estimate and sample from, **including deterministic quantities**. The
+benefit of this design is that the definition of the `sample()` function
+can be arbitrary, allowing the user to either sample from a distribution
+using `numpyro.sample()`, compute fixed quantities (like a mechanistic
+equation), or return a fixed value (like a pre-computed PMF.) For
+instance, when estimating a PMF, the `RandomVariable` sampling function
+may roughly be defined as:
+
+``` python
+# define a new class called MyRandVar that inherits from the RandomVariable class
+class MyRandVar(RandomVariable):
+    #define a method called sample that returns an object of type ArrayLike
+    def sample(...) -> ArrayLike:
+        # calls sample function from NumPyro package
+        return numpyro.sample(...)
+```
+
+Whereas, in some other cases, we may instead use a fixed quantity for
+that variable (like a pre-computed PMF), where the `RandomVariable`’s
+sample function could instead be defined as:
+
+``` python
+# instead define MyRandVar to still inherit from the RandVariable class
+class MyRandVar(RandomVariable):
+    #define sample method that still returns an ArrayLike object
+    def sample(...) -> ArrayLike:
+        #sampling method is a pre-computed PMF, a JAX NumPy array with explicit elements
+        return jax.numpy.array([0.2, 0.7, 0.1])
+```
+
+Thus, when a `Model` samples from `MyRandVar`, it could be either adding
+random variables to be estimated (first case) or just retrieving some
+quantity needed for other calculations (second case.)
+
+The `Model` metaclass provides basic functionality for estimating and
+simulation. Like `RandomVariable`, the `Model` metaclass has a
+`sample()` method that defines the model structure. Ultimately, models
+can be nested (or inherited), providing a straightforward way to add
+layers of complexity. At this stage, the `Model` metaclass consist of
+two model classes `RtInfectionsRenewalModel` which is basic renewal
+model consisting of infections and reproduction numbers and
+`HospitalAdmissionsModel` which includes basic renewal model and
+hospital admissions. In the subsequent sections, we provide examples of
+fitting each of these models.