AI Coding with Haskell

AI Tool Ecosystem for Haskell

Haskell's AI tool ecosystem is functional but noticeably thinner than mainstream languages. GitHub Copilot handles basic Haskell patterns (algebraic data types, pattern matching, do-notation, common type classes) reasonably well. Claude Code demonstrates strong understanding of Haskell's type system and can generate code that type-checks on the first attempt for standard patterns. Cursor provides acceptable completions for straightforward Haskell. The Haskell Language Server (HLS) offers rich type information that AI tools can theoretically leverage, though integration varies. The core challenge is that Haskell's community uses a wide range of abstractions -- from simple IO to complex monad transformer stacks to effect systems -- and AI tools are only reliable for the simpler end of this spectrum. Libraries like lens, servant, and aeson are well-enough represented in training data to get good AI output, but niche libraries or advanced type-level programming are poorly supported.

What AI Does Well with Haskell

• Generating algebraic data types with deriving clauses and exhaustive pattern matching implementations
• Creating aeson FromJSON/ToJSON instances including custom parsing logic and nested object handling
• Producing do-notation blocks with correct monadic sequencing for IO, Maybe, Either, and State
• Writing QuickCheck property tests with generators, Arbitrary instances, and meaningful property definitions

Tips for AI-Assisted Haskell Development

• Always provide type signatures - AI generates much better Haskell code with explicit types
• AI tools handle common monads (IO, Maybe, Either, State) well
• Use AI to generate algebraic data types and pattern matching implementations
• AI understands lens library basics but complex optics may need manual work
• Leverage AI for generating Haskell test cases with QuickCheck properties

Prompting Tips for Haskell

Where AI Struggles with Haskell

• AI tools frequently generate Haskell code that does not compile due to incorrect monad transformer stacking, missing MonadIO lifts, or wrong type class constraints
• Complex type-level Haskell (GADTs, type families, DataKinds, singletons) is generated incorrectly more often than not, requiring significant manual correction
• AI often suggests outdated Haskell patterns (String instead of Text, partial functions like head/fromJust, lazy IO) that experienced Haskell developers avoid
• GHC language extensions are frequently misused in AI-generated code -- the AI may enable extensions that are not needed or fail to enable ones that are required by the generated code

Servant API handler with database access

A Servant API endpoint demonstrating type-safe routing, ReaderT application monad, and database access -- a practical pattern where AI tools can generate compilable Haskell.

{-# LANGUAGE DataKinds #-}
{-# LANGUAGE TypeOperators #-}
{-# LANGUAGE DeriveGeneric #-}

module Api.Users where

import Data.Aeson (ToJSON, FromJSON)
import GHC.Generics (Generic)
import Servant
import Control.Monad.Reader (asks)
import qualified Database.PostgreSQL.Simple as PG

data User = User
  { userId    :: Int
  , userName  :: Text
  , userEmail :: Text
  } deriving (Generic, Show)

instance ToJSON User
instance FromJSON User

type UserAPI =
       "users" :> Get '[JSON] [User]
  :<|> "users" :> Capture "id" Int :> Get '[JSON] User

userHandlers :: ServerT UserAPI AppM
userHandlers = listUsers :<|> getUser

listUsers :: AppM [User]
listUsers = do
  conn <- asks dbConnection
  liftIO $ PG.query_ conn "SELECT id, name, email FROM users"

getUser :: Int -> AppM User
getUser uid = do
  conn <- asks dbConnection
  rows <- liftIO $ PG.query conn "SELECT id, name, email FROM users WHERE id = ?" (PG.Only uid)
  case rows of
    [user] -> pure user
    _      -> throwError err404

Common Use Cases

• Compiler and language tool development
• Financial systems and formal verification
• Backend services with Servant or Yesod
• Domain-specific language implementation

Popular Haskell Libraries AI Handles Well

Best Practices

Haskell's type system is both a blessing and a challenge for AI tools. Provide complete type signatures for all top-level definitions. Stick to well-known libraries and patterns for better AI output. Complex type-level programming (GADTs, type families) may need significant manual adjustment. AI tools handle basic Haskell very well.

Recommended Tools for Haskell

The following AI coding tools offer the best support for Haskell development:

• Cursor - AI-first code editor built as a fork of VS Code with deep AI integration for code generation, editing, and chat.
• GitHub Copilot - AI pair programmer by GitHub and Microsoft that provides code suggestions, chat, and autonomous coding agents directly in your editor.
• Claude Code - Anthropic's agentic CLI coding tool that operates directly in your terminal, capable of editing files, running commands, and managing entire coding workflows.
• Cody - AI coding assistant by Sourcegraph that leverages deep codebase understanding and code search to provide context-aware assistance.

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