diff --git a/README.md b/README.md
index d88d340..8c3a9ee 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,125 @@
-# Lucene cuVS
+# cuVS Lucene
 
 This is a project for using [cuVS](https://github.com/rapidsai/cuvs), NVIDIA's GPU accelerated vector search library, with [Apache Lucene](https://github.com/apache/lucene).
 
-## Overview
+## Contents
 
-This library provides a new [KnnVectorFormat](https://lucene.apache.org/core/10_3_1/core/org/apache/lucene/codecs/KnnVectorsFormat.html) which can be plugged into a Lucene codec.
+1. [What is cuvs-lucene?](#what-is-cuvs-lucene)
+2. [Installing cuvs-lucene](#installing-cuvs-lucene)
+3. [Getting Started](#getting-started)
+4. [Contributing](#contributing)
+5. [References](#references)
 
-## Building
+## What is cuvs-lucene?
+
+`cuvs-lucene` provides a pluggable [KnnVectorsFormat](https://lucene.apache.org/core/10_2_0/core/org/apache/lucene/codecs/KnnVectorsFormat.html) that uses cuVS to offload vector index build — and optionally search — to NVIDIA GPUs. Because it plugs in through a standard Lucene codec, existing Lucene applications can take advantage of GPU acceleration with minimal code changes and gracefully fall back to the default CPU codec when no GPU is present.
+
+Four codecs are currently provided:
+
+- `Lucene101AcceleratedHNSWCodec` — GPU-accelerated HNSW build with CPU HNSW search. The on-disk format is standard Lucene HNSW, so indexes built on the GPU can be read by any stock Lucene 10.x reader.
+  - `LuceneAcceleratedHNSWScalarQuantizedCodec` — scalar-quantized vectors for a smaller index footprint.
+  - `LuceneAcceleratedHNSWBinaryQuantizedCodec` — binary-quantized vectors for an even smaller index footprint.
+- `CuVS2510GPUSearchCodec` — GPU-accelerated HNSW build and GPU search
+
+## Installing cuvs-lucene
 
 ### Prerequisites
-- [CUDA 12.0+](https://developer.nvidia.com/cuda-toolkit-archive),
-- [Maven 3.9.6+](https://maven.apache.org/download.cgi),
+- [CUDA 12.0+](https://developer.nvidia.com/cuda-toolkit-archive)
 - [JDK 22](https://jdk.java.net/archive/)
+- [Maven 3.9.6+](https://maven.apache.org/download.cgi)
+- A compatible cuVS installation (26.04 - 26.06). For Maven usage, install the cuVS tarball and add it to your system library load path. See the cuVS [tarball install instructions](https://docs.rapids.ai/api/cuvs/stable/build/#download-extract).
+
+### Maven
+
+To pull `cuvs-lucene` into a Maven project, add the following dependency to your `pom.xml`:
+
+```xml
+<dependency>
+  <groupId>com.nvidia.cuvs.lucene</groupId>
+  <artifactId>cuvs-lucene</artifactId>
+  <version>26.06.0</version>
+</dependency>
+```
+
+### Building from source
 
 ```sh
+git clone https://github.com/rapidsai/cuvs-lucene.git
+cd cuvs-lucene
 mvn clean compile package
 ```
-The artifacts would be built and available in the target / folder.
 
-### Running Tests
+The resulting artifacts are written to `target/`. To run the tests, first install cuVS and add it to your system library load path, as described in the cuVS [tarball install instructions](https://docs.rapids.ai/api/cuvs/stable/build/#download-extract), then run:
+
+```sh
+mvn clean test
+```
+
+## Getting Started
+
+The example below plugs the GPU-accelerated HNSW codec into a standard Lucene `IndexWriter`. Once the codec is set on the `IndexWriterConfig`, indexing proceeds exactly as it would with the default Lucene codec, and search uses the stock `KnnFloatVectorQuery`.
+
+Before running it, make sure cuVS is installed and available on your system library load path. The cuVS [tarball install instructions](https://docs.rapids.ai/api/cuvs/stable/build/#download-extract) show how to set this up.
+
+In a Maven project that includes the `cuvs-lucene` dependency shown above, create `src/main/java/com/nvidia/cuvs/lucene/examples/HelloCuvsLucene.java`:
+
+```java
+package com.nvidia.cuvs.lucene.examples;
+
+import static org.apache.lucene.index.VectorSimilarityFunction.EUCLIDEAN;
+
+import com.nvidia.cuvs.lucene.AcceleratedHNSWParams;
+import com.nvidia.cuvs.lucene.Lucene101AcceleratedHNSWCodec;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import org.apache.lucene.codecs.Codec;
+import org.apache.lucene.document.Document;
+import org.apache.lucene.document.KnnFloatVectorField;
+import org.apache.lucene.index.IndexWriter;
+import org.apache.lucene.index.IndexWriterConfig;
+import org.apache.lucene.store.Directory;
+import org.apache.lucene.store.FSDirectory;
+
+public class HelloCuvsLucene {
+  public static void main(String[] args) throws Exception {
+    AcceleratedHNSWParams params = new AcceleratedHNSWParams.Builder().build();
+    Codec codec = new Lucene101AcceleratedHNSWCodec(params);
+    IndexWriterConfig config = new IndexWriterConfig().setCodec(codec);
+
+    Path indexPath = Paths.get("index");
+    float[] embedding = new float[] {0.1f, 0.2f, 0.3f, 0.4f};
+
+    try (Directory dir = FSDirectory.open(indexPath);
+        IndexWriter writer = new IndexWriter(dir, config)) {
+      Document doc = new Document();
+      doc.add(new KnnFloatVectorField("vector_field", embedding, EUCLIDEAN));
+      writer.addDocument(doc);
+    }
+
+    System.out.println("Hello cuVS Lucene ran successfully.");
+  }
+}
+```
+
+Run it:
+
 ```sh
-export LD_LIBRARY_PATH={ PATH TO YOUR LOCAL libcuvs_c.so }:$LD_LIBRARY_PATH && mvn clean test
+mvn -q compile org.codehaus.mojo:exec-maven-plugin:3.5.1:java \
+  -Dexec.mainClass=com.nvidia.cuvs.lucene.examples.HelloCuvsLucene
 ```
 
+For more examples, including one that indexes and searches entirely on the GPU using `CuVS2510GPUSearchCodec`, please refer to the [`examples/`](examples) directory.
+
 ## Contributing
 
+If you are interested in contributing to cuvs-lucene, please read our [Contributing guide](CONTRIBUTING.md).
+
 > [!NOTE]
 > The code style format is automatically enforced (including the missing license header, if any) using the [Spotless maven plugin](https://github.com/diffplug/spotless/tree/main/plugin-maven). This currently happens in the maven's `validate` stage.
+
+## References
+
+- [Bring Massive-Scale Vector Search to the GPU with Apache Lucene](https://www.nvidia.com/en-us/on-demand/session/gtc25-S71286/) — NVIDIA GTC 2025 session video
+- [cuVS and Lucene: GPU-based Vector Search](https://www.youtube.com/watch?v=qiW7iIDFJC0) — Berlin Buzzwords 2024 session video
+- [Exploring GPU-accelerated vector search in Elasticsearch with NVIDIA](https://www.elastic.co/search-labs/blog/gpu-accelerated-vector-search-elasticsearch-nvidia) — Elasticsearch Blog
+- [Apache Lucene Accelerated with the NVIDIA cuVS 25.06 Release](https://searchscale.com/blog/apache-lucene-accelerated-with-nvidia-cuvs-25.06-release/) — SearchScale Blog