Define the IR goals: simplicity, analyzability, optimization friendliness, and target independence
Choose an IR form: AST-based, three-address code, SSA, bytecode, or graph-based IR
Specify the IR data model: instructions, operands, types, basic blocks, functions, and modules
Define a stable instruction set: arithmetic, control flow, memory, calls, casts, and comparisons
Represent control flow explicitly with basic blocks and branch instructions
Lower source constructs into simpler IR constructs
Build symbol tables and type information before IR generation
Generate IR from the parsed AST using a recursive visitor or pattern-matching approach
Emit temporaries for intermediate results
Convert expressions into explicit instruction sequences
Convert statements into control-flow blocks and jumps
Create unique labels for branches, loops, and join points
Insert phi nodes if using SSA form
Normalize complex expressions into simpler operations
Handle short-circuit boolean logic with conditional branches
Handle function calls with argument evaluation and return value capture
Handle variable declarations, assignments, and scope management
Map source types to IR types
Insert explicit casts and conversions where needed
Represent memory operations with load and store instructions
Represent arrays, structs, and pointers with address calculation instructions
Preserve source locations for debugging and diagnostics
Validate IR invariants after generation
Run a verifier to check control-flow and type correctness
Build IR in passes if needed: initial lowering, cleanup, SSA construction, optimization prep
Use a builder API to append instructions to the current block
Maintain current function, current block, and insertion point during generation
Split critical edges and normalize branches if required by later passes
Eliminate unreachable blocks after generation
Canonicalize commutative operations and constant expressions
Fold constants during or after IR generation
Choose whether IR is in-memory only or serialized to a file format
Define a clear interface between frontend parsing and IR generation
Test IR generation with small source programs and expected IR snapshots
Compare generated IR against known-good cases for each language feature