Harrison/evaluation notebook (#426)

docs/index.rst

@@ -9,7 +9,7 @@ combine them with other sources of computation or knowledge.
 
 This library is aimed at assisting in the development of those types of applications.
 
-There are five main areas that LangChain is designed to help with.
+There are six main areas that LangChain is designed to help with.
 These are, in increasing order of complexity:
 
 1. LLM and Prompts
@@ -17,6 +17,7 @@ These are, in increasing order of complexity:
 3. Data Augmented Generation
 4. Agents
 5. Memory
+6. [BETA] Evaluation
 
 Let's go through these categories and for each one identify key concepts (to clarify terminology) as well as the problems in this area LangChain helps solve.
 
@@ -107,6 +108,14 @@ both at a short term but also at a long term level. The concept of "Memory" exis
 - A collection of common memory implementations to choose from
 - Common chains/agents that use memory (e.g. chatbots)
 
+**🧐 Evaluation:**
+
+[BETA] Generative models are notoriously hard to evaluate with traditional metrics.
+One new way of evaluating them is using language models themselves to do the evaluation.
+LangChain provides some prompts/chains for assisting in this.
+This is still in Beta, which also means that feedback is especially appreciated here.
+
+
 Documentation Structure
 =======================
 The documentation is structured into the following sections:
@@ -141,6 +150,7 @@ Start here if you haven't used LangChain before.
    examples/data_augmented_generation.rst
    examples/agents.rst
    examples/memory.rst
+   examples/evaluation.rst
    examples/model_laboratory.ipynb
 
 More elaborate examples and walkthroughs of particular