From 52e31c12d4779a31024c7d5f931689d08f243c8c Mon Sep 17 00:00:00 2001
From: paul-gauthier <69695708+paul-gauthier@users.noreply.github.com>
Date: Tue, 23 May 2023 09:30:15 -0700
Subject: [PATCH] Update ctags.md

---
 docs/ctags.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/ctags.md b/docs/ctags.md
index 5d5602e33..05a98ced1 100644
--- a/docs/ctags.md
+++ b/docs/ctags.md
@@ -191,7 +191,7 @@ Some possible approaches to reducing the amount of map data are:
 
   - Distill the global map, to prioritize important symbols and discard "internal" or otherwise less globally relevant identifiers. Possibly enlist `gpt-3.5-turbo` to perform this distillation in a flexible and language agnostic way.
   - Provide a mechanism for GPT to start with a distilled subset of the global map, and let it ask to see more detail about subtrees or keywords that it feels are relevant to the current coding task.
-  - Attempt to analyize the natural language coding task given by the user and predict which subset of the repo map is relevant. Possibly by analysis of prior coding chats within the specific repo. Work on certain files or types of features may require certain somewhat predictable context from elsewhere in the repo. Vector search against the chat history, repo map or codebase may help here.
+  - Attempt to analyize the natural language coding task given by the user and predict which subset of the repo map is relevant. Possibly by analysis of prior coding chats within the specific repo. Work on certain files or types of features may require certain somewhat predictable context from elsewhere in the repo. Vector and keyword search against the chat history, repo map or codebase may help here.
 
 One key goal is to prefer solutions which are language agnostic or
 which can be easily deployed against most popular code languages.