1. Is It Even Possible?
Short answer: Yes, it’s possible in principle, but there are trade-offs:
- Natural Language Parsing Is Hard: Human language is ambiguous. For example:
Sort the list and print each item
Does the user want ascending or descending order? Should duplicates be removed?
- Context and Intent Understanding: The compiler or interpreter would need a robust way to handle incomplete or ambiguous commands. Traditional compilers expect precise syntax. A “prompt-like” language might rely on advanced parsing (potentially AI-based or rule-based) to disambiguate intent.
- Simplicity vs. Power: A super-simple syntax might limit how complex a program can get. Once you need more advanced logic (e.g., concurrency, database connections, or error handling), you risk drifting away from “natural” statements and into “programming constructs.”
However, we already see partial successes of this concept with domain-specific languages (DSLs) and “low-code” or “no-code” tools. These frameworks let users configure business logic with minimal syntax. So, in a sense, it’s not only possible but already happening in certain niches—just not usually labeled as a general-purpose “programming language.”
2. Key Considerations for a “Prompt-Like” Language
Syntax vs. Natural Language
- Minimal, English-like Syntax: You might define statements such as:
read file "data.csv" into dataset
sort dataset by column "Name"
print dataset
- Grammar and Parsing: You’d need a carefully designed grammar to interpret these lines. This grammar might be more flexible than typical languages, but still needs structure:
- Verb → Noun → Optional parameters.
- For instance:
read [resource] ["identifier"] [into variable].
- Disambiguation Mechanisms: When ambiguous, the parser may:
- Prompt the user for clarification, or
- Use a “best guess” approach (e.g., default to ascending order on sort).
Error Handling and Feedback
- Compiler Warnings in Plain English: If the compiler can’t parse a statement or sees a logical error, it might respond with something like:
I see you tried to print a dataset called ‘datset’. Did you mean ‘dataset’?
- Interactive Shell vs. Static Compiler: An interactive REPL or shell might better serve a prompt-like language because it can ask clarifying questions in real-time.
Performance and Resource Usage
- Simplicity in Implementation: A language that uses a straightforward AST (Abstract Syntax Tree) parsing approach can be efficient. You don’t necessarily need a huge runtime or advanced JIT compiler.
- Limit the Scope of the Language: Avoid implementing complex features like concurrency, heavy libraries, or sophisticated reflection if you aim to minimize resource usage. A smaller runtime footprint means less overhead.
Potential Use of AI
- Optional AI-Driven Parsing: A large language model (LLM) or rule-based NLP engine could interpret ambiguous statements. But this might introduce heavier resource usage and dependence on an external service (unless you use a very optimized on-device model).
- Offline, Lean Implementation: Alternatively, a deterministic, rule-based parser can remain lightweight, but you lose some of the “magical” intelligence AI can provide.
3. Conceptual Example
Let’s imagine you define a file named hello.world that contains:
say "Hello, World!"
ask "What is your name?" and store answer in username
say "Nice to meet you, {username}!"
How does the language handle this?
- Lexical Analysis: It identifies tokens like
say, ask, store, and strings.
- Parsing: It matches statements to grammar rules. For example:
say "<message>"ask "<prompt>" and store answer in <variable>
- Execution:
say prints the string to stdout.ask displays a prompt, reads input, assigns it to username.- The second
say uses interpolation to print the user’s name.
This might look like a “prompt-based” DSL. It’s very close to natural language but still restricted to a set of known patterns.
4. How to Keep It Simple but Powerful
Limit the Language Scope
- Focus on a narrow domain (e.g., file I/O, text manipulation, basic logic and conditionals). This ensures you don’t blow up complexity with thousands of features.
Provide Good Defaults
- Whenever the user doesn’t specify details (like sort order), default to something reasonable (e.g., ascending, or lexicographical). This reduces the amount of syntax a user must learn.
Gradual Complexity
- Let advanced users opt into more “programming-like” constructs (loops, functions, conditionals). Keep them in plain-English style but well-defined:
for each item in item_list
say "Item: {item}"
end
Interactive Documentation
- Provide “built-in help” or a REPL that explains commands, like a mini-tutorial.
- Example: Typing
help say prints usage examples.
5. Potential Challenges
- Ambiguity: Natural language often has multiple interpretations. You’ll need to systematically handle or reduce ambiguity (e.g., “sort the list by name ascending” vs. “sort the list by name, ascending or descending?”).
- Scalability: As projects grow bigger, you may need modules, packages, or some method to structure code. Maintaining a “prompt-like” style could become unwieldy.
- Performance Trade-Offs: If you rely on AI-based parsing, you might introduce large model dependencies or external APIs. A purely rule-based approach is simpler but might feel “less intelligent.”
- Community Adoption: Any new language faces the challenge of building an ecosystem of libraries, documentation, and tooling. Without these, it’s hard to gain traction.
6. Path Forward: A Feasible Strategy
- Draft a Small Grammar: Start with a few key verbs: read, write, ask, say, calculate, sort, etc. Let them combine with natural-sounding phrases:
read file "data.csv" into dataset.
- Implement a Lightweight Interpreter: A simple parser that translates each statement into internal actions. Keep it in memory with minimal data structures, ensuring it’s not resource-heavy.
- Offer an Interactive REPL: Let users type commands one by one. Provide immediate feedback or error messages in plain English.
- Gather User Feedback: See where users stumble or want more capabilities. Gradually refine or expand the grammar.
- Expand as Needed, Keep the Core Simple: Possibly add modules for specialized tasks (e.g., HTTP requests, database queries), each with their own small set of natural commands.