Developing Clojure applications with AI assistance introduces a unique challenge: Clojure namespaces are dense, data structures are deeply nested, and code-as-data constructs demand precise context. If you feed entire namespaces and REPL outputs to reasoning-focused LLMs without a strategy, you will burn through your token quotas (and API budget) rapidly.

Here is how to optimize your developer workflow in Antigravity IDE using Gemini’s Thinking Budget configurations.

🧠 Understanding the Reasoning Settings (Thinking Levels)

Modern models (like Gemini 2.5/3.5 Flash and Pro) introduce a configurable Thinking Level (or Reasoning Budget). This setting represents how many internal, step-by-step reasoning tokens the model compiles before writing the final code.

To manage your tokens, you need to understand when to toggle between them:

Setting When to Use Clojure Dev Tasks Quota Consumption Latency
Low / Off Simple, rote coding and direct translations Boilerplate templating, converting JSON to edn, writing basic Babashka shell scripts. Minimal (Input + Output only) Very Fast
Medium Logical, multi-step coding problems Standard app functions, writing test suites, refactoring standard functions. Moderate Moderate
High Highly complex, stateful, or abstract logic Designing advanced macros, debugging race conditions in core.async pipelines, orchestrating multi-agent systems. Maximum (Adds hidden thinking tokens) Slower

🛠️ Tactical Rules for Clojure Token Preservation

1. REPL-Driven Prompting (Don’t Copy-Paste Entire Files)

Instead of copy-pasting your entire core.clj namespace to fix an error: 1. Copy only the function signature and its spec (if you use clojure.spec). 2. Paste the exact stack trace from your REPL. 3. Keep the thinking level on Low. The model doesn’t need to reason about your whole system to fix a syntax bug or structural partition mismatch.

2. Guard Against the Chat History Multiplier

In interactive chats, the IDE sends your entire chat history back to the model on every new turn. If your previous prompts returned responses that consumed 2,000 thinking tokens, those tokens are sent back as input on the next turn. * The Fix: Treat chat threads as transient. Once a bug is resolved or a concept is clear, close the chat thread and start a fresh one. This resets your input context back to zero.

3. Programmatic Control via Babashka

If you write automation scripts that query the Gemini API directly, you can configure the thinking budget dynamically in your payload using generationConfig.

Here is a Babashka script template showing how to switch thinking budgets dynamically:

#!/usr/bin/env bb
(require '[babashka.http-client :as http]
         '[cheshire.core :as json])

(def api-key (System/getenv "GEMINI_API_KEY"))

(defn query-gemini [prompt thinking-budget]
  ;; thinking-budget = 0 (Minimal/Low)
  ;; thinking-budget = 1024 or 2048 (Medium/High reasoning token limit)
  (let [url (str "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=" api-key)
        payload {:contents [{:parts [{:text prompt}]}]
                 :generationConfig 
                 (merge 
                  {:temperature 1.0}
                  (if (zero? thinking-budget)
                    {:thinkingConfig {:thinkingBudget 0}}
                    {:thinkingConfig {:thinkingBudget thinking-budget}}))}]
    (-> (http/post url {:headers {"Content-Type" "application/json"}
                        :body (json/generate-string payload)})
        :body
        (json/parse-string true)
        :candidates
        first
        :content
        :parts
        first
        :text)))

;; Example: Run a low-cost, fast query
(println (query-gemini "Generate a basic Babashka task definition for file-syncing." 0))

🎯 Conclusion

Treating LLM usage like hardware memory management makes you a highly efficient developer: * Use Low by default for 90% of your coding and rapid chatting. * Elevate to High reasoning exclusively for architectural bottlenecks or macro debugging. * Keep your chat history short.